AirBnB & Zillow Data Challenge
Vidhi Rathod
27 April 2020
Exploring most profitable zipcodes within New York
1.0 Case Overview
1.1 Executive Summary
You are consulting for a real estate company that has a role in purchasing properties to rent out short-term as part of their business model specifically within New York City. The real estate company has already concluded that two-bedroom properties are the most profitable; however, they do not know which zip codes are the best to invest in.
The real estate company has engaged your firm to build out a data product and provide your conclusions to help them understand which zip codes would generate the most profit on short term rentals within New York City.
You will be looking at publicly available data from Zillow and AirBnB:
Cost data: Zillow provides us an estimate of value for two-bedroom properties
Revenue data: AirBnB is the medium through which the investor plans to lease out their investment property. Fortunately for you, we can see how many properties in certain neighbourhoods rent out for in New York City.
Results: Analysing the data under different dimensions of Customer Preference, Market Demand, Customer Feedback/Reviews, Break-Even Time, Three zip codes that are proposed suitable for investment are : 11215, 11217,11231
1.2 Given Assumptions
After meeting with the strategy team, you’ve got an idea of where to start, key concerns, and how you can help this real estate company with the market data while keeping the following assumptions in mind :
- You can assume an occupancy rate of 75% or you can come up with your model to calculate occupancy;
- The investor will pay for the property in cash.
- The time value of money discount rate is 0%.
- All properties and all square feet within each locale can be assumed to be homogeneous
1.3 Taken Assumptions
Below Assumptions are considered :
- Actual Rent/Night of property includes rental price in addition to a mandatory Cleaning fee.
- Cleaning fee and security deposit is considered as 0 where Blank
- Seasonality has little or no impact on the number of bookings.
- An increase in the Cost of property post 2017-July had no seasonality effect(Time series has no seasonality).
- Rent of all properties increases by 10% every year.
- The number of reviews are indicative of the high occupancy of the property.
- All reviews are positive.
- Availablity for the next 365 days: availablity_365 is a status quo as far as occupancy in concerned.
Less availability indicates High demand.
2.0 Initial Set Up
2.1 Package Loading
Required Packages
- Tidyverse (dplyr, ggplot2..) - Data Read, Manipulation and visualisation
- Plotly - Interactive Visualization
- KableExtra - Styling for table (Styling Data Tables within Markdown)
- gridExtra- Graphical arrangement
- forecast- Time series and forecasting
- ggplot2- Graphical representation
- stringr- string manipulations
- corrplot-making correlogram
- knitrr- Dynamic report generation
2.2 Data Loading
Revenue(Airbnb) and Cost(Zillow) Datasets are loaded into R-Environment with the help of below code..
Please note the below code uses .csv files to load data
costPrice_Zillow<-read.csv("Zip_Zhvi_2bedroom.csv")
rentAirbnbListings<-read.csv("listings.csv")
dim_zlow<-dim(costPrice_Zillow)
dim_abnb<-dim(rentAirbnbListings)
dim_zlow #Zillow data## [1] 8946 262dim_abnb #Airbnb data## [1] 48895 106Check Dimensions
The dimension of Zillow Cost data is 8946, 262 The dimension of Airbnb Revenue data is 48895, 106
3.0 Data Preparation
Cost and Revenue datasets are scrubbed separately in an attempt to enrich the data quality for Exploratory data analysis.
3.1 Zillow Cost data
kable(head(costPrice_Zillow)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| RegionID | RegionName | City | State | Metro | CountyName | SizeRank | X1996.04 | X1996.05 | X1996.06 | X1996.07 | X1996.08 | X1996.09 | X1996.10 | X1996.11 | X1996.12 | X1997.01 | X1997.02 | X1997.03 | X1997.04 | X1997.05 | X1997.06 | X1997.07 | X1997.08 | X1997.09 | X1997.10 | X1997.11 | X1997.12 | X1998.01 | X1998.02 | X1998.03 | X1998.04 | X1998.05 | X1998.06 | X1998.07 | X1998.08 | X1998.09 | X1998.10 | X1998.11 | X1998.12 | X1999.01 | X1999.02 | X1999.03 | X1999.04 | X1999.05 | X1999.06 | X1999.07 | X1999.08 | X1999.09 | X1999.10 | X1999.11 | X1999.12 | X2000.01 | X2000.02 | X2000.03 | X2000.04 | X2000.05 | X2000.06 | X2000.07 | X2000.08 | X2000.09 | X2000.10 | X2000.11 | X2000.12 | X2001.01 | X2001.02 | X2001.03 | X2001.04 | X2001.05 | X2001.06 | X2001.07 | X2001.08 | X2001.09 | X2001.10 | X2001.11 | X2001.12 | X2002.01 | X2002.02 | X2002.03 | X2002.04 | X2002.05 | X2002.06 | X2002.07 | X2002.08 | X2002.09 | X2002.10 | X2002.11 | X2002.12 | X2003.01 | X2003.02 | X2003.03 | X2003.04 | X2003.05 | X2003.06 | X2003.07 | X2003.08 | X2003.09 | X2003.10 | X2003.11 | X2003.12 | X2004.01 | X2004.02 | X2004.03 | X2004.04 | X2004.05 | X2004.06 | X2004.07 | X2004.08 | X2004.09 | X2004.10 | X2004.11 | X2004.12 | X2005.01 | X2005.02 | X2005.03 | X2005.04 | X2005.05 | X2005.06 | X2005.07 | X2005.08 | X2005.09 | X2005.10 | X2005.11 | X2005.12 | X2006.01 | X2006.02 | X2006.03 | X2006.04 | X2006.05 | X2006.06 | X2006.07 | X2006.08 | X2006.09 | X2006.10 | X2006.11 | X2006.12 | X2007.01 | X2007.02 | X2007.03 | X2007.04 | X2007.05 | X2007.06 | X2007.07 | X2007.08 | X2007.09 | X2007.10 | X2007.11 | X2007.12 | X2008.01 | X2008.02 | X2008.03 | X2008.04 | X2008.05 | X2008.06 | X2008.07 | X2008.08 | X2008.09 | X2008.10 | X2008.11 | X2008.12 | X2009.01 | X2009.02 | X2009.03 | X2009.04 | X2009.05 | X2009.06 | X2009.07 | X2009.08 | X2009.09 | X2009.10 | X2009.11 | X2009.12 | X2010.01 | X2010.02 | X2010.03 | X2010.04 | X2010.05 | X2010.06 | X2010.07 | X2010.08 | X2010.09 | X2010.10 | X2010.11 | X2010.12 | X2011.01 | X2011.02 | X2011.03 | X2011.04 | X2011.05 | X2011.06 | X2011.07 | X2011.08 | X2011.09 | X2011.10 | X2011.11 | X2011.12 | X2012.01 | X2012.02 | X2012.03 | X2012.04 | X2012.05 | X2012.06 | X2012.07 | X2012.08 | X2012.09 | X2012.10 | X2012.11 | X2012.12 | X2013.01 | X2013.02 | X2013.03 | X2013.04 | X2013.05 | X2013.06 | X2013.07 | X2013.08 | X2013.09 | X2013.10 | X2013.11 | X2013.12 | X2014.01 | X2014.02 | X2014.03 | X2014.04 | X2014.05 | X2014.06 | X2014.07 | X2014.08 | X2014.09 | X2014.10 | X2014.11 | X2014.12 | X2015.01 | X2015.02 | X2015.03 | X2015.04 | X2015.05 | X2015.06 | X2015.07 | X2015.08 | X2015.09 | X2015.10 | X2015.11 | X2015.12 | X2016.01 | X2016.02 | X2016.03 | X2016.04 | X2016.05 | X2016.06 | X2016.07 | X2016.08 | X2016.09 | X2016.10 | X2016.11 | X2016.12 | X2017.01 | X2017.02 | X2017.03 | X2017.04 | X2017.05 | X2017.06 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 61639 | 10025 | New York | NY | New York | New York | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 798600 | 798800 | 801500 | 804600 | 814900 | 828300 | 835700 | 849300 | 858100 | 854000 | 834800 | 821700 | 830300 | 853700 | 868300 | 875200 | 882200 | 892400 | 905000 | 924000 | 934400 | 932100 | 927500 | 923600 | 907900 | 890900 | 883400 | 896100 | 923900 | 952900 | 964600 | 972500 | 973800 | 973400 | 966500 | 966800 | 967100 | 974800 | 976800 | 976100 | 973700 | 974500 | 973200 | 966400 | 950400 | 933300 | 920900 | 909400 | 891400 | 873300 | 858800 | 850200 | 842800 | 834000 | 828800 | 821400 | 813900 | 813300 | 821500 | 831700 | 845100 | 854500 | 858900 | 859200 | 863500 | 876000 | 886100 | 890000 | 894200 | 901800 | 909500 | 913300 | 907400 | 900000 | 897700 | 896300 | 892300 | 890400 | 888600 | 891700 | 899500 | 904400 | 908200 | 914000 | 915100 | 912300 | 914000 | 921100 | 923300 | 917300 | 915000 | 922800 | 929100 | 937700 | 955700 | 974200 | 995500 | 1019500 | 1035100 | 1054900 | 1079900 | 1092600 | 1103500 | 1118800 | 1139300 | 1154600 | 1144100 | 1120300 | 1125500 | 1136000 | 1135100 | 1130000 | 1138200 | 1153700 | 1174800 | 1185400 | 1188400 | 1189700 | 1193700 | 1199900 | 1201400 | 1202600 | 1214200 | 1235200 | 1258000 | 1287700 | 1307200 | 1313900 | 1317100 | 1327400 | 1338800 | 1350400 | 1356600 | 1358500 | 1364000 | 1373300 | 1382600 | 1374400 | 1364100 | 1366300 | 1354800 | 1327500 | 1317300 | 1333700 | 1352100 | 1390000 | 1431000 |
| 84654 | 60657 | Chicago | IL | Chicago | Cook | 2 | 167700 | 166400 | 166700 | 167200 | 166900 | 166900 | 168000 | 170100 | 171700 | 173000 | 174600 | 177600 | 180100 | 182300 | 184400 | 186300 | 187600 | 189400 | 190300 | 189700 | 189800 | 191900 | 194500 | 195500 | 196000 | 196900 | 198900 | 201400 | 204600 | 207900 | 211800 | 214600 | 216000 | 217500 | 220200 | 222800 | 226200 | 229600 | 232400 | 234400 | 236300 | 238300 | 241800 | 246100 | 249500 | 251300 | 253200 | 255700 | 259200 | 263100 | 266600 | 269500 | 272800 | 275500 | 278800 | 283400 | 288600 | 291300 | 292400 | 294600 | 297100 | 298200 | 299800 | 302000 | 304200 | 307900 | 311000 | 311400 | 311000 | 311700 | 312300 | 312000 | 311800 | 312600 | 313000 | 314400 | 317300 | 319700 | 320500 | 321000 | 321600 | 323800 | 326100 | 329000 | 332200 | 334700 | 336000 | 337300 | 337500 | 337100 | 334900 | 333100 | 332800 | 333400 | 335100 | 337800 | 339400 | 340700 | 343200 | 345000 | 345200 | 344600 | 344500 | 346200 | 349800 | 353400 | 355100 | 356300 | 358300 | 359500 | 359200 | 358500 | 359100 | 361400 | 364700 | 367500 | 369400 | 369800 | 369600 | 368700 | 368100 | 369000 | 370300 | 371700 | 374200 | 375700 | 376400 | 378200 | 379800 | 380100 | 380400 | 381200 | 382700 | 382700 | 380200 | 377800 | 376300 | 375600 | 376000 | 376200 | 375800 | 376300 | 377200 | 376800 | 373700 | 370100 | 368700 | 368600 | 366600 | 362200 | 358600 | 355300 | 352300 | 350900 | 350100 | 347900 | 345400 | 343400 | 342800 | 342600 | 341100 | 339900 | 338900 | 338200 | 335200 | 329800 | 325500 | 323600 | 323400 | 325000 | 325800 | 323200 | 320100 | 318600 | 317400 | 315700 | 315000 | 315300 | 315600 | 313900 | 309800 | 305700 | 301800 | 299500 | 299900 | 301100 | 300300 | 298900 | 298500 | 298500 | 297000 | 296800 | 298700 | 299600 | 300700 | 303900 | 306800 | 307500 | 308500 | 310000 | 310800 | 311200 | 313000 | 315800 | 319000 | 323400 | 327500 | 330000 | 331800 | 334500 | 336000 | 335700 | 335400 | 336300 | 338800 | 342400 | 344400 | 344000 | 343900 | 345100 | 346100 | 346900 | 348000 | 349700 | 351200 | 351700 | 350700 | 350400 | 352000 | 354300 | 355900 | 356500 | 355200 | 353800 | 353700 | 354600 | 356200 | 357800 | 358200 | 358500 | 360300 | 362400 | 363700 | 365200 | 367100 | 368600 | 370200 | 372300 | 375300 | 378700 | 381400 | 381800 | 382100 | 383300 | 385100 |
| 61637 | 10023 | New York | NY | New York | New York | 3 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1526800 | 1424500 | 1346600 | 1331300 | 1322500 | 1289300 | 1265400 | 1249700 | 1241100 | 1232700 | 1225500 | 1228200 | 1252600 | 1266100 | 1288700 | 1308100 | 1333000 | 1356400 | 1362000 | 1353600 | 1364000 | 1373900 | 1389600 | 1401600 | 1404100 | 1415800 | 1432400 | 1455400 | 1474200 | 1462300 | 1438300 | 1435500 | 1427800 | 1411200 | 1407400 | 1419700 | 1457400 | 1500800 | 1524900 | 1537800 | 1558700 | 1586100 | 1602300 | 1621100 | 1639300 | 1657400 | 1657400 | 1656100 | 1649400 | 1643400 | 1632400 | 1618200 | 1588300 | 1543600 | 1500800 | 1464200 | 1426100 | 1387300 | 1362600 | 1351700 | 1344300 | 1331800 | 1334800 | 1314200 | 1271900 | 1252300 | 1262300 | 1279200 | 1309000 | 1335300 | 1353800 | 1366400 | 1372100 | 1381300 | 1385000 | 1388100 | 1399100 | 1399800 | 1389300 | 1384700 | 1380900 | 1367900 | 1365400 | 1375100 | 1380400 | 1377000 | 1375100 | 1379000 | 1395200 | 1414500 | 1419000 | 1403100 | 1383200 | 1376700 | 1378200 | 1378700 | 1375900 | 1366700 | 1365500 | 1382200 | 1404700 | 1428000 | 1445700 | 1452900 | 1460100 | 1484400 | 1508400 | 1522800 | 1538300 | 1568600 | 1597400 | 1622900 | 1654300 | 1684600 | 1713000 | 1728800 | 1736100 | 1745900 | 1753800 | 1736600 | 1730400 | 1734500 | 1728700 | 1720800 | 1717700 | 1700100 | 1680400 | 1676400 | 1685600 | 1708100 | 1730400 | 1751800 | 1778300 | 1810400 | 1831600 | 1844400 | 1861600 | 1889600 | 1901500 | 1895300 | 1890200 | 1898400 | 1924500 | 1967300 | 1993500 | 1980700 | 1960900 | 1951300 | 1937800 | 1929800 | 1955000 | 2022400 | 2095000 | 2142300 |
| 84616 | 60614 | Chicago | IL | Chicago | Cook | 4 | 195800 | 193500 | 192600 | 192300 | 192600 | 193600 | 195500 | 197600 | 199400 | 201300 | 203600 | 206500 | 209200 | 211100 | 212600 | 214400 | 215600 | 216500 | 217900 | 220100 | 222200 | 223900 | 225400 | 227700 | 230100 | 231700 | 232700 | 233700 | 234700 | 235600 | 236800 | 238800 | 240800 | 242400 | 243800 | 246400 | 250200 | 254300 | 257600 | 261100 | 264800 | 267900 | 270700 | 272800 | 274400 | 276200 | 278600 | 280100 | 283100 | 287700 | 293600 | 298500 | 302700 | 305000 | 306800 | 309400 | 313100 | 314900 | 316200 | 318200 | 320600 | 322900 | 325500 | 328400 | 330700 | 332800 | 334400 | 335900 | 337400 | 339700 | 342300 | 343800 | 343400 | 342300 | 341800 | 341700 | 342400 | 344300 | 346900 | 348900 | 350200 | 351700 | 353500 | 355700 | 358000 | 361600 | 364000 | 365500 | 366400 | 367000 | 365200 | 363100 | 362000 | 362500 | 364800 | 368200 | 370800 | 371400 | 371200 | 371800 | 374200 | 377800 | 380100 | 381700 | 384400 | 386600 | 385700 | 385400 | 388500 | 392200 | 394500 | 394900 | 394800 | 395500 | 400000 | 404300 | 407600 | 409500 | 410400 | 408700 | 406400 | 403800 | 402400 | 402300 | 403000 | 403100 | 403600 | 404500 | 406100 | 407700 | 408700 | 409600 | 409800 | 408700 | 408600 | 411000 | 412200 | 411400 | 410200 | 408400 | 406600 | 406500 | 406400 | 404600 | 402900 | 400600 | 397500 | 392600 | 387500 | 383700 | 381000 | 378900 | 377700 | 377400 | 376700 | 374000 | 369600 | 366200 | 364500 | 364100 | 364300 | 363600 | 361900 | 361900 | 356400 | 347100 | 342400 | 344000 | 345200 | 346900 | 346100 | 342600 | 340100 | 339900 | 338600 | 335500 | 333900 | 335000 | 336000 | 334200 | 330300 | 327000 | 326000 | 326100 | 326700 | 326300 | 324400 | 322700 | 323200 | 322800 | 320700 | 319500 | 320100 | 320500 | 321800 | 323600 | 324300 | 324100 | 324700 | 326000 | 327600 | 329800 | 332600 | 336800 | 342300 | 348100 | 353600 | 358900 | 361900 | 363900 | 366200 | 368300 | 369800 | 371400 | 372400 | 373200 | 373800 | 374800 | 376200 | 376800 | 376300 | 374900 | 373800 | 373900 | 374700 | 375300 | 375000 | 374700 | 376300 | 378100 | 378000 | 377700 | 378300 | 380000 | 383100 | 385900 | 388100 | 389700 | 391800 | 393400 | 394700 | 394900 | 395700 | 396400 | 397500 | 398900 | 401200 | 403200 | 405700 | 408300 | 408800 | 408000 | 410100 | 412200 | 412200 |
| 93144 | 79936 | El Paso | TX | El Paso | El Paso | 5 | 59100 | 60500 | 60900 | 60800 | 60300 | 60400 | 61200 | 61700 | 61000 | 60100 | 59300 | 59000 | 58700 | 58400 | 58000 | 57800 | 57900 | 57800 | 57800 | 58100 | 58400 | 58700 | 59200 | 59400 | 58700 | 58100 | 57900 | 57900 | 57700 | 57600 | 57500 | 57800 | 58000 | 58000 | 57900 | 57800 | 57800 | 58000 | 58500 | 58700 | 59000 | 59200 | 59300 | 59500 | 59900 | 60300 | 60400 | 60300 | 60300 | 60000 | 59500 | 59400 | 59800 | 59900 | 59700 | 59500 | 59400 | 59500 | 59700 | 59700 | 59200 | 58700 | 58400 | 57800 | 57000 | 56700 | 56800 | 56600 | 56500 | 56500 | 56600 | 56700 | 56600 | 56700 | 57000 | 57300 | 57300 | 57300 | 57100 | 56900 | 56900 | 57000 | 56700 | 56700 | 57000 | 57400 | 57700 | 58000 | 58300 | 58600 | 58800 | 59100 | 59500 | 60200 | 61100 | 61700 | 62400 | 63100 | 63600 | 63800 | 64400 | 64900 | 65100 | 65200 | 65300 | 65600 | 66300 | 67100 | 67900 | 68800 | 69600 | 70600 | 71500 | 72100 | 72600 | 73400 | 74200 | 74500 | 75000 | 75700 | 76400 | 77100 | 77700 | 78100 | 78700 | 79600 | 80600 | 81500 | 82300 | 83200 | 83900 | 84400 | 84300 | 84400 | 85200 | 86100 | 86800 | 87400 | 87700 | 87800 | 88300 | 88800 | 88500 | 87900 | 87500 | 86600 | 85500 | 84600 | 84200 | 83900 | 83600 | 83400 | 83500 | 84100 | 84600 | 84600 | 84700 | 84600 | 84600 | 85100 | 85500 | 85800 | 86600 | 87600 | 86500 | 84200 | 83300 | 83800 | 83800 | 83600 | 83600 | 83400 | 82900 | 82400 | 82200 | 82100 | 82300 | 82800 | 82900 | 82600 | 82700 | 83100 | 83300 | 83100 | 82800 | 82500 | 82300 | 82200 | 82300 | 82200 | 81900 | 81700 | 82100 | 82600 | 82900 | 83000 | 83000 | 82900 | 82100 | 81200 | 80800 | 80700 | 81200 | 81800 | 81800 | 81400 | 81400 | 81500 | 81900 | 82000 | 81900 | 81900 | 82100 | 82100 | 81500 | 80800 | 80300 | 80100 | 80100 | 80700 | 81200 | 81700 | 81900 | 81700 | 81500 | 81700 | 81700 | 80900 | 81000 | 81500 | 81400 | 80500 | 80000 | 80100 | 80500 | 80800 | 81400 | 82300 | 82600 | 82600 | 82500 | 82500 | 82600 | 82700 | 82600 | 82400 | 82300 | 82400 | 82300 | 82500 | 83200 | 83900 | 84100 | 83900 | 83700 |
| 84640 | 60640 | Chicago | IL | Chicago | Cook | 6 | 123300 | 122600 | 122000 | 121500 | 120900 | 120600 | 120900 | 121300 | 121600 | 122100 | 122900 | 124200 | 125300 | 126100 | 126700 | 127900 | 129300 | 130400 | 131300 | 131700 | 132300 | 133500 | 134500 | 134800 | 135200 | 135500 | 136300 | 137700 | 139600 | 141600 | 143400 | 144500 | 145600 | 147400 | 149200 | 149900 | 150500 | 151700 | 152800 | 153900 | 156400 | 159400 | 161800 | 163800 | 165700 | 167100 | 168400 | 169600 | 171000 | 172400 | 174800 | 177900 | 180400 | 182300 | 184600 | 187700 | 190700 | 193100 | 196100 | 200000 | 202900 | 205500 | 207600 | 208600 | 209200 | 210000 | 210200 | 211300 | 213000 | 214100 | 215200 | 217600 | 220200 | 222400 | 224600 | 227000 | 228600 | 230100 | 232400 | 234300 | 235300 | 236200 | 237000 | 237900 | 239200 | 241300 | 243600 | 244200 | 243800 | 244500 | 245500 | 245700 | 246400 | 247500 | 248000 | 249000 | 251100 | 252900 | 253700 | 256100 | 259400 | 261300 | 262200 | 263800 | 265300 | 267600 | 270500 | 272800 | 273700 | 273600 | 273200 | 273300 | 273500 | 273800 | 274300 | 274400 | 274400 | 275300 | 276600 | 278200 | 280600 | 283100 | 284400 | 284200 | 283000 | 282000 | 282400 | 283700 | 285200 | 286700 | 286700 | 284900 | 283300 | 282700 | 282100 | 280800 | 280300 | 280200 | 280100 | 279900 | 281200 | 282300 | 282100 | 279300 | 275800 | 273100 | 272600 | 271800 | 270300 | 268600 | 266900 | 264800 | 262900 | 260700 | 258500 | 256600 | 255000 | 252900 | 250600 | 248900 | 248900 | 250100 | 250300 | 250400 | 248000 | 243800 | 240200 | 239700 | 239000 | 238200 | 236800 | 235400 | 233300 | 231300 | 230300 | 228200 | 225200 | 223100 | 222300 | 220700 | 218300 | 215900 | 213200 | 210900 | 209600 | 208200 | 205900 | 204000 | 203200 | 202500 | 200800 | 199400 | 199900 | 201900 | 204500 | 207000 | 208100 | 207100 | 205300 | 204700 | 204700 | 205800 | 208600 | 211800 | 213500 | 213800 | 215100 | 218400 | 221500 | 223900 | 226100 | 227900 | 229100 | 230200 | 230600 | 230400 | 230000 | 229000 | 227600 | 226100 | 225700 | 226200 | 226500 | 226500 | 227100 | 227800 | 229400 | 231800 | 234100 | 235400 | 235100 | 233900 | 233700 | 235300 | 237200 | 238500 | 239300 | 239600 | 239500 | 240200 | 242700 | 244900 | 247700 | 249500 | 248800 | 247000 | 247300 | 248700 | 250800 | 252800 | 253800 | 253800 | 253400 | 254100 | 255100 |
Please note Only 6 rows are shown above with head function
Structure of Zillow data
#Check the structure
str(costPrice_Zillow)## 'data.frame': 8946 obs. of 262 variables:
## $ RegionID : int 61639 84654 61637 84616 93144 84640 97564 91982 71831 84646 ...
## $ RegionName: int 10025 60657 10023 60614 79936 60640 94109 77494 32162 60647 ...
## $ City : Factor w/ 4684 levels "Aberdeen","Abilene",..: 2702 649 2702 649 1119 649 3479 672 3912 649 ...
## $ State : Factor w/ 48 levels "AK","AL","AR",..: 34 15 34 15 42 15 5 42 10 15 ...
## $ Metro : Factor w/ 467 levels "","Aberdeen",..: 292 76 292 76 125 76 368 188 415 76 ...
## $ CountyName: Factor w/ 722 levels "Ada","Adams",..: 460 158 460 158 212 158 574 231 634 158 ...
## $ SizeRank : int 1 2 3 4 5 6 7 8 9 10 ...
## $ X1996.04 : int NA 167700 NA 195800 59100 123300 336800 117300 72600 129600 ...
## $ X1996.05 : int NA 166400 NA 193500 60500 122600 337400 117900 74800 129800 ...
## $ X1996.06 : int NA 166700 NA 192600 60900 122000 337700 118800 77300 129900 ...
## $ X1996.07 : int NA 167200 NA 192300 60800 121500 338200 121100 80000 130600 ...
## $ X1996.08 : int NA 166900 NA 192600 60300 120900 339700 123200 81700 131100 ...
## $ X1996.09 : int NA 166900 NA 193600 60400 120600 340400 124600 82600 131200 ...
## $ X1996.10 : int NA 168000 NA 195500 61200 120900 339700 125800 83500 131000 ...
## $ X1996.11 : int NA 170100 NA 197600 61700 121300 338900 126400 84700 131400 ...
## $ X1996.12 : int NA 171700 NA 199400 61000 121600 338300 125600 85100 132000 ...
## $ X1997.01 : int NA 173000 NA 201300 60100 122100 340000 125100 84200 132100 ...
## $ X1997.02 : int NA 174600 NA 203600 59300 122900 345800 127000 82200 132200 ...
## $ X1997.03 : int NA 177600 NA 206500 59000 124200 350600 129400 79900 133100 ...
## $ X1997.04 : int NA 180100 NA 209200 58700 125300 351300 130400 78400 133500 ...
## $ X1997.05 : int NA 182300 NA 211100 58400 126100 352600 130400 77500 133300 ...
## $ X1997.06 : int NA 184400 NA 212600 58000 126700 357300 131200 76800 134100 ...
## $ X1997.07 : int NA 186300 NA 214400 57800 127900 363900 130600 77400 136100 ...
## $ X1997.08 : int NA 187600 NA 215600 57900 129300 370600 129100 78600 138700 ...
## $ X1997.09 : int NA 189400 NA 216500 57800 130400 374300 127600 79800 141300 ...
## $ X1997.10 : int NA 190300 NA 217900 57800 131300 375600 125800 80700 143100 ...
## $ X1997.11 : int NA 189700 NA 220100 58100 131700 381000 124500 81300 144100 ...
## $ X1997.12 : int NA 189800 NA 222200 58400 132300 389500 124600 81500 144800 ...
## $ X1998.01 : int NA 191900 NA 223900 58700 133500 395400 125100 81900 145300 ...
## $ X1998.02 : int NA 194500 NA 225400 59200 134500 400800 125500 82300 145800 ...
## $ X1998.03 : int NA 195500 NA 227700 59400 134800 409000 126000 82200 146100 ...
## $ X1998.04 : int NA 196000 NA 230100 58700 135200 414600 127200 81700 146500 ...
## $ X1998.05 : int NA 196900 NA 231700 58100 135500 418500 128700 81300 147500 ...
## $ X1998.06 : int NA 198900 NA 232700 57900 136300 424200 128300 81000 148500 ...
## $ X1998.07 : int NA 201400 NA 233700 57900 137700 430500 126300 80500 149200 ...
## $ X1998.08 : int NA 204600 NA 234700 57700 139600 437500 126000 80300 150000 ...
## $ X1998.09 : int NA 207900 NA 235600 57600 141600 448900 126500 80100 150500 ...
## $ X1998.10 : int NA 211800 NA 236800 57500 143400 460300 126300 79900 151100 ...
## $ X1998.11 : int NA 214600 NA 238800 57800 144500 461900 127400 80600 152000 ...
## $ X1998.12 : int NA 216000 NA 240800 58000 145600 461900 130600 82300 153700 ...
## $ X1999.01 : int NA 217500 NA 242400 58000 147400 466200 131600 83800 156300 ...
## $ X1999.02 : int NA 220200 NA 243800 57900 149200 470500 130400 84700 159200 ...
## $ X1999.03 : int NA 222800 NA 246400 57800 149900 470500 129900 85600 161000 ...
## $ X1999.04 : int NA 226200 NA 250200 57800 150500 473100 131900 86900 162300 ...
## $ X1999.05 : int NA 229600 NA 254300 58000 151700 480000 134600 88300 163500 ...
## $ X1999.06 : int NA 232400 NA 257600 58500 152800 487800 137400 88900 164700 ...
## $ X1999.07 : int NA 234400 NA 261100 58700 153900 496000 140600 89300 165700 ...
## $ X1999.08 : int NA 236300 NA 264800 59000 156400 506900 141800 91000 166800 ...
## $ X1999.09 : int NA 238300 NA 267900 59200 159400 516300 141200 93400 169600 ...
## $ X1999.10 : int NA 241800 NA 270700 59300 161800 522900 141800 95100 173700 ...
## $ X1999.11 : int NA 246100 NA 272800 59500 163800 533800 142800 95600 177200 ...
## $ X1999.12 : int NA 249500 NA 274400 59900 165700 549400 141700 95000 179100 ...
## $ X2000.01 : int NA 251300 NA 276200 60300 167100 564600 141200 95500 180900 ...
## $ X2000.02 : int NA 253200 NA 278600 60400 168400 577300 143000 98700 183600 ...
## $ X2000.03 : int NA 255700 NA 280100 60300 169600 592200 145600 101800 187200 ...
## $ X2000.04 : int NA 259200 NA 283100 60300 171000 610400 145500 102500 190700 ...
## $ X2000.05 : int NA 263100 NA 287700 60000 172400 626700 145400 102300 193500 ...
## $ X2000.06 : int NA 266600 NA 293600 59500 174800 640600 145000 103300 195700 ...
## $ X2000.07 : int NA 269500 NA 298500 59400 177900 654400 143000 103300 196500 ...
## $ X2000.08 : int NA 272800 NA 302700 59800 180400 663500 140800 101900 197800 ...
## $ X2000.09 : int NA 275500 NA 305000 59900 182300 664700 140000 101800 200200 ...
## $ X2000.10 : int NA 278800 NA 306800 59700 184600 670100 142200 102700 202700 ...
## $ X2000.11 : int NA 283400 NA 309400 59500 187700 679000 145400 102500 204800 ...
## $ X2000.12 : int NA 288600 NA 313100 59400 190700 676500 146400 103100 208000 ...
## $ X2001.01 : int NA 291300 NA 314900 59500 193100 663100 145900 105000 211800 ...
## $ X2001.02 : int NA 292400 NA 316200 59700 196100 656800 145800 105300 214400 ...
## $ X2001.03 : int NA 294600 NA 318200 59700 200000 658700 142400 104500 215700 ...
## $ X2001.04 : int NA 297100 NA 320600 59200 202900 661300 138200 104300 218100 ...
## $ X2001.05 : int NA 298200 NA 322900 58700 205500 661100 135200 103900 221100 ...
## $ X2001.06 : int NA 299800 NA 325500 58400 207600 658700 133600 103000 222100 ...
## $ X2001.07 : int NA 302000 NA 328400 57800 208600 653900 133500 103000 222100 ...
## $ X2001.08 : int NA 304200 NA 330700 57000 209200 646800 135400 103500 222900 ...
## $ X2001.09 : int NA 307900 NA 332800 56700 210000 641400 136800 103500 224500 ...
## $ X2001.10 : int NA 311000 NA 334400 56800 210200 636300 136500 103700 226700 ...
## $ X2001.11 : int NA 311400 NA 335900 56600 211300 630300 135300 104000 229700 ...
## $ X2001.12 : int NA 311000 NA 337400 56500 213000 628500 134600 104000 231500 ...
## $ X2002.01 : int NA 311700 NA 339700 56500 214100 633500 135400 104700 232200 ...
## $ X2002.02 : int NA 312300 NA 342300 56600 215200 637300 137700 106800 233100 ...
## $ X2002.03 : int NA 312000 NA 343800 56700 217600 640700 139500 109700 234300 ...
## $ X2002.04 : int NA 311800 NA 343400 56600 220200 647900 139900 112100 235100 ...
## $ X2002.05 : int NA 312600 NA 342300 56700 222400 655500 140600 114300 235500 ...
## $ X2002.06 : int NA 313000 NA 341800 57000 224600 662500 141600 116600 236400 ...
## $ X2002.07 : int NA 314400 NA 341700 57300 227000 670800 141200 118000 238100 ...
## $ X2002.08 : int NA 317300 NA 342400 57300 228600 677500 140600 117800 239300 ...
## $ X2002.09 : int NA 319700 NA 344300 57300 230100 687900 141900 117800 240400 ...
## $ X2002.10 : int NA 320500 NA 346900 57100 232400 698400 142700 118600 242100 ...
## $ X2002.11 : int NA 321000 NA 348900 56900 234300 701000 141700 119900 243700 ...
## $ X2002.12 : int NA 321600 NA 350200 56900 235300 698900 141700 121300 244900 ...
## $ X2003.01 : int NA 323800 NA 351700 57000 236200 697600 143800 121900 247000 ...
## $ X2003.02 : int NA 326100 NA 353500 56700 237000 691800 144100 121100 249500 ...
## $ X2003.03 : int NA 329000 NA 355700 56700 237900 684600 143000 120400 252100 ...
## $ X2003.04 : int NA 332200 NA 358000 57000 239200 681100 142200 120600 254300 ...
## $ X2003.05 : int NA 334700 NA 361600 57400 241300 685100 141400 121200 257200 ...
## $ X2003.06 : int NA 336000 NA 364000 57700 243600 692200 141000 121600 261300 ...
## $ X2003.07 : int NA 337300 NA 365500 58000 244200 697500 141400 122400 265400 ...
## $ X2003.08 : int NA 337500 NA 366400 58300 243800 704200 140800 123900 268400 ...
## $ X2003.09 : int NA 337100 NA 367000 58600 244500 710500 139400 125300 269600 ...
## $ X2003.10 : int NA 334900 NA 365200 58800 245500 717200 139400 126200 268700 ...
## $ X2003.11 : int NA 333100 NA 363100 59100 245700 732300 140200 127600 268100 ...
## [list output truncated]Check Missing values in Columns
#Check Missing values in each column
colSums(is.na(costPrice_Zillow))## RegionID RegionName City State Metro CountyName
## 0 0 0 0 0 0
## SizeRank X1996.04 X1996.05 X1996.06 X1996.07 X1996.08
## 0 2662 2582 2582 2577 2576
## X1996.09 X1996.10 X1996.11 X1996.12 X1997.01 X1997.02
## 2576 2576 2566 2566 2542 2113
## X1997.03 X1997.04 X1997.05 X1997.06 X1997.07 X1997.08
## 2093 2093 2093 2091 2091 1994
## X1997.09 X1997.10 X1997.11 X1997.12 X1998.01 X1998.02
## 1991 1991 1988 1984 1967 1822
## X1998.03 X1998.04 X1998.05 X1998.06 X1998.07 X1998.08
## 1821 1865 1973 1961 1776 1742
## X1998.09 X1998.10 X1998.11 X1998.12 X1999.01 X1999.02
## 1742 1730 1709 1707 1706 1689
## X1999.03 X1999.04 X1999.05 X1999.06 X1999.07 X1999.08
## 1688 1688 1685 1675 1675 1652
## X1999.09 X1999.10 X1999.11 X1999.12 X2000.01 X2000.02
## 1652 1652 1652 1652 1652 1633
## X2000.03 X2000.04 X2000.05 X2000.06 X2000.07 X2000.08
## 1633 1631 1631 1631 1626 1598
## X2000.09 X2000.10 X2000.11 X2000.12 X2001.01 X2001.02
## 1598 1598 1598 1598 1679 1580
## X2001.03 X2001.04 X2001.05 X2001.06 X2001.07 X2001.08
## 1553 1552 1552 1551 1551 1546
## X2001.09 X2001.10 X2001.11 X2001.12 X2002.01 X2002.02
## 1546 1546 1545 1545 1545 1538
## X2002.03 X2002.04 X2002.05 X2002.06 X2002.07 X2002.08
## 1538 1537 1537 1537 1537 1524
## X2002.09 X2002.10 X2002.11 X2002.12 X2003.01 X2003.02
## 1522 1521 1521 1521 1520 1499
## X2003.03 X2003.04 X2003.05 X2003.06 X2003.07 X2003.08
## 1498 1498 1498 1498 1498 1485
## X2003.09 X2003.10 X2003.11 X2003.12 X2004.01 X2004.02
## 1484 1483 1482 1480 1479 1447
## X2004.03 X2004.04 X2004.05 X2004.06 X2004.07 X2004.08
## 1428 1428 1428 1426 1426 1403
## X2004.09 X2004.10 X2004.11 X2004.12 X2005.01 X2005.02
## 1398 1392 1389 1389 1388 1321
## X2005.03 X2005.04 X2005.05 X2005.06 X2005.07 X2005.08
## 1305 1303 1303 1303 1303 1288
## X2005.09 X2005.10 X2005.11 X2005.12 X2006.01 X2006.02
## 1288 1286 1285 1285 1284 1260
## X2006.03 X2006.04 X2006.05 X2006.06 X2006.07 X2006.08
## 1260 1259 1259 1259 1259 1250
## X2006.09 X2006.10 X2006.11 X2006.12 X2007.01 X2007.02
## 1249 1248 1248 1248 1247 1242
## X2007.03 X2007.04 X2007.05 X2007.06 X2007.07 X2007.08
## 1240 1239 1239 1238 1238 1226
## X2007.09 X2007.10 X2007.11 X2007.12 X2008.01 X2008.02
## 1226 1226 1226 1226 1226 1225
## X2008.03 X2008.04 X2008.05 X2008.06 X2008.07 X2008.08
## 1225 1225 1225 1225 1225 1194
## X2008.09 X2008.10 X2008.11 X2008.12 X2009.01 X2009.02
## 1194 1194 1194 1194 1193 1190
## X2009.03 X2009.04 X2009.05 X2009.06 X2009.07 X2009.08
## 1184 1178 1178 1146 1146 1095
## X2009.09 X2009.10 X2009.11 X2009.12 X2010.01 X2010.02
## 1095 1095 1094 1091 1091 1082
## X2010.03 X2010.04 X2010.05 X2010.06 X2010.07 X2010.08
## 971 959 944 923 903 177
## X2010.09 X2010.10 X2010.11 X2010.12 X2011.01 X2011.02
## 177 174 174 174 163 151
## X2011.03 X2011.04 X2011.05 X2011.06 X2011.07 X2011.08
## 149 149 149 147 134 121
## X2011.09 X2011.10 X2011.11 X2011.12 X2012.01 X2012.02
## 120 120 120 118 108 93
## X2012.03 X2012.04 X2012.05 X2012.06 X2012.07 X2012.08
## 91 91 91 87 84 67
## X2012.09 X2012.10 X2012.11 X2012.12 X2013.01 X2013.02
## 66 65 65 65 64 31
## X2013.03 X2013.04 X2013.05 X2013.06 X2013.07 X2013.08
## 26 21 21 20 20 0
## X2013.09 X2013.10 X2013.11 X2013.12 X2014.01 X2014.02
## 0 0 0 0 0 0
## X2014.03 X2014.04 X2014.05 X2014.06 X2014.07 X2014.08
## 0 0 0 0 0 0
## X2014.09 X2014.10 X2014.11 X2014.12 X2015.01 X2015.02
## 0 0 0 0 0 0
## X2015.03 X2015.04 X2015.05 X2015.06 X2015.07 X2015.08
## 0 0 0 0 0 0
## X2015.09 X2015.10 X2015.11 X2015.12 X2016.01 X2016.02
## 0 0 0 0 0 18
## X2016.03 X2016.04 X2016.05 X2016.06 X2016.07 X2016.08
## 18 18 18 0 0 0
## X2016.09 X2016.10 X2016.11 X2016.12 X2017.01 X2017.02
## 0 0 0 3 0 0
## X2017.03 X2017.04 X2017.05 X2017.06
## 0 0 0 0Observations
- The dimension of Zillow Cost data is 8946, 262.
- Data only available untill July 2017.Hence, forecast of Cost price for Jan 2020 will bring accuracy to the analysis.
- Table structure: data is horizontal form, which is an indicator or messy format data. Converting this to long-form will be helpful but the horizontal form will be helpful in forecast function.
- Check column names for redundancy: Many columns are redundant such as RegionID, State, Metro, CountyName.
- Missing Values: Cost price is missing until a certain timeline, this can be the time from when this zipcode was included in the zillow database.
Below methodology is used to scrub the Zillow data
Scrubbing Methodology
- Check for NA Values in columns
- Current price Jan 2020 for each zipcode, hence we decide to use the horizontal format of data for ease of working in Time series pattern.
- Filter the Zillow file for City=Newyork but the names seem to have many levels for each city. Hence it will better to Inner Join the listings file for each zipcode to Zillow file. JOining is handled post scrubbing phase.
- Zillow file only has 2 bedroom listings.
- Remove ‘X’ from numeric price column names.
- Column name ‘RegionName’ is changed ‘Zipcode’
Below is the scrubbing function that is used for Zillow cost data
#Scrubbing & Forecast function
zillow_scrub <- function(ZillowData,cityName){
ZillowData$City<-str_trim(ZillowData$City)
Zlow_cty<-ZillowData%>% filter(str_detect(tolower(ZillowData$City), cityName))
Zlow_cty <- Zlow_cty[,c(2,3,7:ncol(Zlow_cty))]#RegionName City SizeRank
colnames(Zlow_cty)<-str_replace(colnames(Zlow_cty),"X","")
Zlow_cty$currentPrice <- 0
for(i in 1:nrow(Zlow_cty)){
start_pt<-sum(is.na(Zlow_cty[i,-(1:3)])) +1+3 #start point in full dataset
end_pt<-ncol(Zlow_cty)-1#end point in full dataset - Current price
n<-end_pt-start_pt+1
strdt<-colnames(Zlow_cty[i,c(start_pt:end_pt)])[1]
yr<-as.numeric(substr(strdt,0,4))
mn<-as.numeric(substr(strdt,6,7))
#Convert monthly cost data into time series data
tmp = ts(as.vector(t(Zlow_cty[,c(start_pt:end_pt)])[,i]),start = c(yr,mn),frequency = 12) # Convert the
# Use forecast package auto ARIMA function for prediction
fit<-auto.arima(tmp)
#########################################################################
predval<-data.frame(forecast(fit,h=31))["Jan 2020","Point.Forecast"]
Zlow_cty$currentPrice[i] <- predval
}
Zlow_cty<-rename(Zlow_cty,zipcode=RegionName)
Zlow_cty<-Zlow_cty[,c('zipcode','currentPrice')]
return(Zlow_cty) # return the filtered data containing only relevant columns
}
cityName<-"new york"
#Function call
zlow_scbd<-zillow_scrub(costPrice_Zillow,cityName)
dimPostScrubZlw<-dim(zlow_scbd)# Dimensions of Zillow data post scrubbing
dimPostScrubZlw## [1] 26 2Dimensions of Zillow data post scrubbing: 26, 2
Zillow data post scrubbing
kable(head(zlow_scbd)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| zipcode | currentPrice |
|---|---|
| 10025 | 1717145 |
| 10023 | 3009982 |
| 10128 | 3093119 |
| 10011 | 2897002 |
| 10003 | 2336587 |
| 11201 | 1628300 |
Please note only 6 rows have been shown above with head function We are only interested in Cost price by Zipcode, hence we are pulling only relevant columns
Result
- Current price for current month Jan 2020 is predicted.
3.2 Airbnb Revenue data: Listing
kable(head(rentAirbnbListings)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| id | listing_url | scrape_id | last_scraped | name | summary | space | description | experiences_offered | neighborhood_overview | notes | transit | access | interaction | house_rules | thumbnail_url | medium_url | picture_url | xl_picture_url | host_id | host_url | host_name | host_since | host_location | host_about | host_response_time | host_response_rate | host_acceptance_rate | host_is_superhost | host_thumbnail_url | host_picture_url | host_neighbourhood | host_listings_count | host_total_listings_count | host_verifications | host_has_profile_pic | host_identity_verified | street | neighbourhood | neighbourhood_cleansed | neighbourhood_group_cleansed | city | state | zipcode | market | smart_location | country_code | country | latitude | longitude | is_location_exact | property_type | room_type | accommodates | bathrooms | bedrooms | beds | bed_type | amenities | square_feet | price | weekly_price | monthly_price | security_deposit | cleaning_fee | guests_included | extra_people | minimum_nights | maximum_nights | minimum_minimum_nights | maximum_minimum_nights | minimum_maximum_nights | maximum_maximum_nights | minimum_nights_avg_ntm | maximum_nights_avg_ntm | calendar_updated | has_availability | availability_30 | availability_60 | availability_90 | availability_365 | calendar_last_scraped | number_of_reviews | number_of_reviews_ltm | first_review | last_review | review_scores_rating | review_scores_accuracy | review_scores_cleanliness | review_scores_checkin | review_scores_communication | review_scores_location | review_scores_value | requires_license | license | jurisdiction_names | instant_bookable | is_business_travel_ready | cancellation_policy | require_guest_profile_picture | require_guest_phone_verification | calculated_host_listings_count | calculated_host_listings_count_entire_homes | calculated_host_listings_count_private_rooms | calculated_host_listings_count_shared_rooms | reviews_per_month |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2539 | https://www.airbnb.com/rooms/2539 | 2.019071e+13 | 2019-07-09 | Clean & quiet apt home by the park | Renovated apt home in elevator building. | Spacious, renovated, and clean apt home, one block to F train, 25 minutes to lower Manhatten | Renovated apt home in elevator building. Spacious, renovated, and clean apt home, one block to F train, 25 minutes to lower Manhatten Close to Prospect Park and Historic Ditmas Park Very close to F and G trains and Express bus into NY. The B and Q are closeby also. If this room is unavailable on your desired dates, check out our other rooms, such as: https://www.airbnb.com/rooms/10267242 | none | Close to Prospect Park and Historic Ditmas Park | If this room is unavailable on your desired dates, check out our other rooms, such as: https://www.airbnb.com/rooms/10267242 | Very close to F and G trains and Express bus into NY. The B and Q are closeby also. | -The security and comfort of all our guests is important to us! Therefore, no one is permitted to check in without first emailing a clear government issued photo ID, acceptable to manager. Instructions will be provided in the house manual. -No eating, drinking or storage of food in the room. -No smoking. -Illicit drug use is strictly forbidden. -Quiet hours after 10pm and before 8am, This is respectful for our neighbors and other guests. -Please clean up after yourself when using the kitchen and bath. -Please lock the doors and close the windows when exiting the home. -Please indicate and pay for the correct number of guests. Failure to do so will cancel your reservation with no refund due. -Please remove your shoes when entering the apt home. Thanks for choosing our home! | NA | NA | https://a0.muscache.com/im/pictures/3949d073-a02e-4ebc-aa9c-ac74f00eaa1f.jpg?aki_policy=large | NA | 2787 | https://www.airbnb.com/users/show/2787 | John | 2008-09-07 | New York, New York, United States | Educated professional living in Brooklyn. I love meeting new people, running, hiking, fine foods, traveling, etc. One of my favorite trips was spending New Year’s Eve in London on the Thames River. Big Ben, spectacular fireworks and light show; and fun times with a good crowd of international tourists. A most memorable night and trip! Also, I generally approach life with a positive attitude. I look forward to meeting you. | within an hour | 100% | N/A | f | https://a0.muscache.com/im/pictures/8674565a-758d-476b-a580-4d99ea9baab9.jpg?aki_policy=profile_small | https://a0.muscache.com/im/pictures/8674565a-758d-476b-a580-4d99ea9baab9.jpg?aki_policy=profile_x_medium | Gravesend | 6 | 6 | [‘email’, ‘phone’, ‘reviews’, ‘kba’] | t | t | Brooklyn , NY, United States | Brooklyn | Kensington | Brooklyn | Brooklyn | NY | 11218 | New York | Brooklyn , NY | US | United States | 40.64749 | -73.97237 | f | Apartment | Private room | 2 | 1 | 1 | 1 | Real Bed | {TV,“Cable TV”,Internet,Wifi,“Wheelchair accessible”,Kitchen,“Free parking on premises”,Elevator,“Free street parking”,“Buzzer/wireless intercom”,Heating,“Suitable for events”,Washer,Dryer,“Smoke detector”,“Carbon monoxide detector”,“First aid kit”,“Safety card”,“Fire extinguisher”,Essentials,Shampoo,“24-hour check-in”,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“translation missing: en.hosting_amenity_49”,“translation missing: en.hosting_amenity_50”,“Self check-in”,Keypad,“Outlet covers”,“Hot water”,“Bed linens”,“Extra pillows and blankets”,Microwave,“Coffee maker”,Refrigerator,“Dishes and silverware”,“Cooking basics”,Oven,Stove,“Luggage dropoff allowed”,“Long term stays allowed”,“Cleaning before checkout”} | NA | $149.00 | $299.00 | $999.00 | $100.00 | $25.00 | 1 | $35.00 | 1 | 730 | 1 | 1 | 730 | 730 | 1 | 730 | 3 weeks ago | t | 30 | 60 | 90 | 365 | 2019-07-09 | 9 | 2 | 2015-12-04 | 2018-10-19 | 98 | 10 | 10 | 10 | 10 | 10 | 10 | f | f | f | moderate | f | f | 6 | 0 | 5 | 1 | 0.21 | ||||
| 2595 | https://www.airbnb.com/rooms/2595 | 2.019071e+13 | 2019-07-09 | Skylit Midtown Castle | Find your romantic getaway to this beautiful, spacious skylit studio in the heart of Midtown, Manhattan. STUNNING SKYLIT STUDIO / 1 BED + SINGLE / FULL BATH / FULL KITCHEN / FIREPLACE / CENTRALLY LOCATED / WiFi + APPLE TV / SHEETS + TOWELS |
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Find your romantic getaway to this beautiful, spacious skylit studio in the heart of Midtown, Manhattan. STUNNING SKYLIT STUDIO / 1 BED + SINGLE / FULL BATH / FULL KITCHEN / FIREPLACE / CENTRALLY LOCATED / WiFi + APPLE TV / SHEETS + TOWELS - Spacious (500+ft²), immaculate and nicely furnished & designed studio. - Tuck yourself into the ultra comfortable bed under the skylight. Fall in love with a myriad of bright lights in the city night sky. - Single-sized bed/convertible floor mattress with luxury bedding (available upon request). - Gorgeous pyramid skylight with amazing diffused natural light, stunning architectural details, soaring high vaulted ceilings, exposed brick, wood burning fireplace, floor seating area with natural zafu cushions, modern style mixed with eclectic art & antique treasures, large full bath, newly renovated kitchen, air conditioning/heat, high speed WiFi Internet, and Apple TV. - Centrally located in the heart of Midtown Manhattan just a few blocks from all s | none | Centrally located in the heart of Manhattan just a few blocks from all subway connections in the very desirable Midtown location a few minutes walk to Times Square, the Theater District, Bryant Park and Herald Square. | Apartment is located on 37th Street between 5th & 6th Avenue, just a few blocks from all subway connections. Closest Subways (in order of proximity to apartment (Website hidden by Airbnb) W: 34th Street & 6th Avenu (Website hidden by Airbnb) 3: 34th Street & 7th Avenue 7: 42nd & 5th Avenu (Website hidden by Airbnb) S: 42nd Street between Park & Lexington Avenue (Website hidden by Airbnb) E: 34th Street and 8th Avenue If coming by car, there is a parking garage on the block and free street parking. | Guests have full access to the kitchen, bathroom and living spaces. The closets are private/off limits. | I am a Sound Therapy Practitioner and Kundalini Yoga & Meditation teacher. I work with energy and sound for relaxation and healing, using Symphonic gong, singing bowls, tuning forks, drums, voice and other instruments. Sound relaxation sessions and/or personalized Kundalini Yoga sessions are available in the space upon request. Individual, couples or group sessions available. Licensed acupuncture and massage also available upon request. Please inquire. I welcome my guests at the apartment for check-in, or alternatively, a self check-in can be arranged. Once you are settled in, I am just a phone call, text or email away, should you have any questions, concerns or issues during your stay. My desire is that you have a smooth arrival and amazing stay here. | Make yourself at home, respect the space and the neighbors. No pets, no smoking and no unauthorized guests. | NA | NA | https://a0.muscache.com/im/pictures/f0813a11-40b2-489e-8217-89a2e1637830.jpg?aki_policy=large | NA | 2845 | https://www.airbnb.com/users/show/2845 | Jennifer | 2008-09-09 | New York, New York, United States | A New Yorker since 2000! My passion is creating beautiful, unique spaces where unforgettable memories are made. It’s my pleasure to host people from around the world and meet new faces. Welcome travelers! I am a Sound Therapy Practitioner and Kundalini Yoga & Meditation teacher. I work with energy and sound for relaxation and healing, using Symphonic gong, singing bowls, tuning forks, drums, voice and other instruments. | within a few hours | 87% | N/A | f | https://a0.muscache.com/im/users/2845/profile_pic/1259095067/original.jpg?aki_policy=profile_small | https://a0.muscache.com/im/users/2845/profile_pic/1259095067/original.jpg?aki_policy=profile_x_medium | Midtown | 5 | 5 | [‘email’, ‘phone’, ‘reviews’, ‘kba’, ‘work_email’] | t | t | New York, NY, United States | Manhattan | Midtown | Manhattan | New York | NY | 10018 | New York | New York, NY | US | United States | 40.75362 | -73.98377 | f | Apartment | Entire home/apt | 2 | 1 | 0 | 1 | Real Bed | {TV,Wifi,“Air conditioning”,Kitchen,“Paid parking off premises”,“Free street parking”,“Indoor fireplace”,Heating,“Family/kid friendly”,“Smoke detector”,“Carbon monoxide detector”,“Fire extinguisher”,Essentials,Shampoo,“Lock on bedroom door”,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“Self check-in”,Keypad,“Private living room”,Bathtub,“Hot water”,“Bed linens”,“Extra pillows and blankets”,“Ethernet connection”,“Coffee maker”,Refrigerator,“Dishes and silverware”,“Cooking basics”,Oven,Stove,“Luggage dropoff allowed”,“Long term stays allowed”,“Cleaning before checkout”,“Wide entrance for guests”,“Flat path to guest entrance”,“Well-lit path to entrance”,“No stairs or steps to enter”} | NA | $225.00 | $1,995.00 | $350.00 | $100.00 | 2 | $0.00 | 1 | 1125 | 1 | 1 | 1125 | 1125 | 1 | 1125 | 4 days ago | t | 25 | 55 | 80 | 355 | 2019-07-09 | 45 | 11 | 2009-11-21 | 2019-05-21 | 95 | 10 | 9 | 10 | 10 | 10 | 9 | f | f | f | strict_14_with_grace_period | t | t | 2 | 1 | 0 | 1 | 0.38 | ||||
| 3647 | https://www.airbnb.com/rooms/3647 | 2.019071e+13 | 2019-07-08 | THE VILLAGE OF HARLEM….NEW YORK ! | WELCOME TO OUR INTERNATIONAL URBAN COMMUNITY This Spacious 1 bedroom is with Plenty of Windows with a View……. Sleeps…..Four Adults…..two in the Livingrm. with (2) Sofa-beds. (Website hidden by Airbnb) two in the Bedrm.on a very Comfortable Queen Size Bed… A Complete Bathrm…..With Shower and Bathtub……. Fully Equipped with Linens & Towels…….. Spacious Living Room……Flat ScreenTelevision…..DVD Player with Movies available for your viewing during your stay………………………………………………………………….. Dining Area…..for Morning Coffee or Tea…………………………………………….. The Kitchen Area is Modern with Granite Counter Top… includes the use of a Coffee Maker…Microwave to Heat up a Carry Out/In Meal…. Not suited for a Gourmet Cook…or Top Chef……Sorry!!!! . This Flat is located in HISTORIC HARLEM…. near the Appollo Theater and The Museum Mile…on Fifth Avenue. Sylvia’s World Famous Resturant…loca | WELCOME TO OUR INTERNATIONAL URBAN COMMUNITY This Spacious 1 bedroom is with Plenty of Windows with a View……. Sleeps…..Four Adults…..two in the Livingrm. with (2) Sofa-beds. (Website hidden by Airbnb) two in the Bedrm.on a very Comfortable Queen Size Bed… A Complete Bathrm…..With Shower and Bathtub……. Fully Equipped with Linens & Towels…….. Spacious Living Room……Flat ScreenTelevision…..DVD Player with Movies available for your viewing during your stay………………………………………………………………….. Dining Area…..for Morning Coffee or Tea…………………………………………….. The Kitchen Area is Modern with Granite Counter Top… includes the use of a Coffee Maker…Microwave to Heat up a Carry Out/In Meal…. Not suited for a Gourmet Cook…or Top Chef……Sorry!!!! . This Flat is located in HISTORIC HARLEM…. near the Appollo Theater and The Museum Mile…on Fifth Avenue. Sylvia’s World Famous Resturant…loca | none | Upon arrival please have a legibile copy of your Passport and / or State Photo. ID. as well as your confirmation letter. Please NO SMOKING …LOUD TALKING or PARTIES of any kind. Security Deposit and Cleaning Fees in CASH at time of arrival. Security deposit will be refunded within 72hrs pending no damages. .Cleaning fees are non-refundable. At Check Out… Please dispose of all trash/garbage in the bins located outside behind the entrance stairs. Please place all dirty linens and towels in the dirty laundry bin. Please don’t leave any dishes in the sink and dispose of all Plastic Dishes/Plastic Culinary. If you need a late check-out please contact the Emergency Contact Telephone Numbers that are listed in the Flat. PLEASE LEAVE ALL KEYS IN THE FLAT and BE CERTAIN THE DOORS ARE LOCKED. WE HOPE YOU ENJOYED YOUR STAY and will write positve comments and will visit again. | NA | NA | https://a0.muscache.com/im/pictures/838341/9b3c66f3_original.jpg?aki_policy=large | NA | 4632 | https://www.airbnb.com/users/show/4632 | Elisabeth | 2008-11-25 | New York, New York, United States | Make Up Artist National/ (Website hidden by Airbnb) Production. I m curently working with a Production Company in WDC and Coordinated a “Day Of Service” for the Presidential Innaugration 2013. I can’t live without Starbucks and Oprah’s Chai Tea Latte…and my circle of of great friends world wide. “BLESS ME INTO USEFULLNESS” is my daily prayer. I | within a day | 100% | N/A | f | https://a0.muscache.com/im/users/4632/profile_pic/1328402497/original.jpg?aki_policy=profile_small | https://a0.muscache.com/im/users/4632/profile_pic/1328402497/original.jpg?aki_policy=profile_x_medium | Harlem | 1 | 1 | [‘email’, ‘phone’, ‘google’, ‘reviews’, ‘jumio’, ‘government_id’] | t | t | New York, NY, United States | Harlem | Harlem | Manhattan | New York | NY | 10027 | New York | New York, NY | US | United States | 40.80902 | -73.94190 | t | Apartment | Private room | 2 | 1 | 1 | 1 | Pull-out Sofa | {“Cable TV”,Internet,Wifi,“Air conditioning”,Kitchen,“Buzzer/wireless intercom”,Heating,“Smoke detector”,“Carbon monoxide detector”,“translation missing: en.hosting_amenity_49”,“translation missing: en.hosting_amenity_50”} | NA | $150.00 | $200.00 | $75.00 | 2 | $20.00 | 3 | 7 | 3 | 3 | 7 | 7 | 3 | 7 | 34 months ago | t | 30 | 60 | 90 | 365 | 2019-07-08 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | f | f | f | strict_14_with_grace_period | t | t | 1 | 0 | 1 | 0 | NA | ||||||||||||
| 3831 | https://www.airbnb.com/rooms/3831 | 2.019071e+13 | 2019-07-09 | Cozy Entire Floor of Brownstone | Urban retreat: enjoy 500 s.f. floor in 1899 brownstone, with wood and ceramic flooring throughout (completed Aug. 2015 through Sept. 2015), roomy bdrm, & upgraded kitchen & bathroom (completed Oct. 2015). It’s sunny and loaded with everything you need! | Greetings! We own a double-duplex brownstone in Clinton Hill on Gates near Classon Avenue - (7 blocks to C train, 5 blocks to G train, minutes to all), in which we host on the entire top floor of the upper duplex. This is more of an efficiency set-up: it is the top floor on a two-family, double duplex brownstone. The top floor for our guests consists of a sizable bedroom, full bath and eat-in kitchen for your exclusive use. Our family occupies the floors below. You go through a common hallway and staircase, to get to the top floor (2 easy flights up from the main entrance), but not through any rooms, so it is a fairly private set-up. - Clinton Hill, Gates Avenue near Classon Ave. (1 mi. or less to Williamsburg, Park Slope, Prospect Heights, downtown, Ft. Greene, Bed-Stuy, Bushwick; 20 mins to Manhattan) - includes FiOS, heat (or A/C), hot water, and electricity all included - furnished with two twin beds (convertible into a king bed), one rollaway twin bed and one inflatable | Urban retreat: enjoy 500 s.f. floor in 1899 brownstone, with wood and ceramic flooring throughout (completed Aug. 2015 through Sept. 2015), roomy bdrm, & upgraded kitchen & bathroom (completed Oct. 2015). It’s sunny and loaded with everything you need! Greetings! We own a double-duplex brownstone in Clinton Hill on Gates near Classon Avenue - (7 blocks to C train, 5 blocks to G train, minutes to all), in which we host on the entire top floor of the upper duplex. This is more of an efficiency set-up: it is the top floor on a two-family, double duplex brownstone. The top floor for our guests consists of a sizable bedroom, full bath and eat-in kitchen for your exclusive use. Our family occupies the floors below. You go through a common hallway and staircase, to get to the top floor (2 easy flights up from the main entrance), but not through any rooms, so it is a fairly private set-up. - Clinton Hill, Gates Avenue near Classon Ave. (1 mi. or less to Williamsburg, Park Slope, Pro | none | Just the right mix of urban center and local neighborhood; close to all but enough quiet for a calming walk. | B52 bus for a 10-minute ride to downtown Brooklyn is a few yards away on the corner; G train/Classon Avenue is 5 blocks away; C train is about 6 blocks to either the Clinton/Washington stop or Franklin Avenue stop. There is on-street parking, alternate side is twice per week on the immediate block but only once per week on Classon. From LaGuardia Airport, a taxi will cost $30-$35, but there is also a bus that will put you at the Jackson Heights subway station, and from there it’s about 5 stops to catch the G train, which stops 5 blocks away. From JFK, the taxi is closer to $40, but the AirTran can get you conveniently to the A/C line and the C train is about 6 blocks from here. From JFK via subway/metro/train: From JFK take the AirTrain to Howard Beach to catch the A train toward Brooklyn/Manhattan. Take the A train to Utica Avenue and go across that same platform to catch the C local train (you could also transfer at Nostrand but you would have to carry luggage downstairs to cat | You will have exclusive use of and access to: a sizable private room as described in “The Space” section, furnished with two twin beds (which we will combine into one king bed upon request) and optional rollaway twin and/or inflatable beds, and other small furnishings; full private bath and private eat-in kitchen both renovated in Fall 2015; sizable dining table in sun-filled kitchen area doubles as a great desk space; alcove perfect for vertical bike storage. Upon request you may also have some use of the livingroom on the floor just below. | We’ll be around, but since you have the top floor to yourself, most of the interaction is on the way in or out - we’re open to socializing and did so frequently with our last long-term guests, so it’s really up to you | Smoking - outside please; pets allowed but please contact me first for arrangements | NA | NA | https://a0.muscache.com/im/pictures/e49999c2-9fd5-4ad5-b7cc-224deac989aa.jpg?aki_policy=large | NA | 4869 | https://www.airbnb.com/users/show/4869 | LisaRoxanne | 2008-12-07 | New York, New York, United States | Laid-back bi-coastal actor/professor/attorney. | within a few hours | 93% | N/A | f | https://a0.muscache.com/im/users/4869/profile_pic/1371927771/original.jpg?aki_policy=profile_small | https://a0.muscache.com/im/users/4869/profile_pic/1371927771/original.jpg?aki_policy=profile_x_medium | Clinton Hill | 1 | 1 | [‘email’, ‘phone’, ‘reviews’, ‘kba’] | t | t | Brooklyn, NY, United States | Brooklyn | Clinton Hill | Brooklyn | Brooklyn | NY | 11238 | New York | Brooklyn, NY | US | United States | 40.68514 | -73.95976 | t | Guest suite | Entire home/apt | 3 | 1 | 1 | 4 | Real Bed | {TV,“Cable TV”,Internet,Wifi,“Air conditioning”,Kitchen,“Pets allowed”,“Free street parking”,Heating,“Family/kid friendly”,“Smoke detector”,“Carbon monoxide detector”,“Fire extinguisher”,Essentials,Shampoo,“Lock on bedroom door”,“24-hour check-in”,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“Self check-in”,Lockbox,Bathtub,“High chair”,“Stair gates”,“Children’s books and toys”,“Pack ’n Play/travel crib”,“Hot water”,“Luggage dropoff allowed”,“Long term stays allowed”} | 500 | $89.00 | $575.00 | $2,100.00 | $500.00 | 1 | $0.00 | 1 | 730 | 1 | 1 | 730 | 730 | 1 | 730 | today | t | 0 | 0 | 3 | 194 | 2019-07-09 | 270 | 69 | 2014-09-30 | 2019-07-05 | 90 | 10 | 9 | 10 | 10 | 10 | 9 | f | f | f | moderate | f | f | 1 | 1 | 0 | 0 | 4.64 | ||||
| 5022 | https://www.airbnb.com/rooms/5022 | 2.019071e+13 | 2019-07-08 | Entire Apt: Spacious Studio/Loft by central park | Loft apartment with high ceiling and wood flooring located 10 minutes away from Central Park in Harlem -Â 1 block away from 6 train and 3 blocks from 2 & 3 line. This is in a recently renovated building which includes elevator, trash shoot. marble entrance and laundromat in the basement. The apartment is a spacious loft studio. The seating area and sleeping area is divided by a bookcase. There is a long hallway entrance where the bathroom and closet for your clothes is situated. The apartment is in mint condition, the walls have been freshly painted a few months ago. Supermarket, and 24 hour convenience store less than 1 block away. 1 block away from Hot Yoga Studio and NY Sports club facility. Perfect for anyone wanting to stay in Manhattan but get more space. 10 minutes away from midtown and 15 minutes away from downtown. The neighborhood is lively and diverse. You will need to travel at least 10 blocks to find cafe’s, restaurants etc.. There are a few restaurants on 100 street on | Loft apartment with high ceiling and wood flooring located 10 minutes away from Central Park in Harlem -Â 1 block away from 6 train and 3 blocks from 2 & 3 line. This is in a recently renovated building which includes elevator, trash shoot. marble entrance and laundromat in the basement. The apartment is a spacious loft studio. The seating area and sleeping area is divided by a bookcase. There is a long hallway entrance where the bathroom and closet for your clothes is situated. The apartment is in mint condition, the walls have been freshly painted a few months ago. Supermarket, and 24 hour convenience store less than 1 block away. 1 block away from Hot Yoga Studio and NY Sports club facility. Perfect for anyone wanting to stay in Manhattan but get more space. 10 minutes away from midtown and 15 minutes away from downtown. The neighborhood is lively and diverse. You will need to travel at least 10 blocks to find cafe’s, restaurants etc.. There are a few restaurants on 100 street on | none | Please be considerate when staying in the apartment. This is a low key building and it’s important guest are respectful. You can come and go as you please I just ask that you keep a low profile. 1) Please be respectful of neighbors - no loud music after 10pm and keep a low profile 2) Do not open the door for anyone 3) Please keep the apt clean 4) No access to mailbox - please forward personal mail to job or school | NA | NA | https://a0.muscache.com/im/pictures/feb453bd-fdec-405c-8bfa-3f6963d827e9.jpg?aki_policy=large | NA | 7192 | https://www.airbnb.com/users/show/7192 | Laura | 2009-01-29 | Miami, Florida, United States | I have been a NYer for almost 10 years. I came to NY to study and never left. I work in the advertising industry and love to eat peanut butter & jelly sandwiches. | N/A | N/A | N/A | f | https://a0.muscache.com/im/users/7192/profile_pic/1325651676/original.jpg?aki_policy=profile_small | https://a0.muscache.com/im/users/7192/profile_pic/1325651676/original.jpg?aki_policy=profile_x_medium | East Harlem | 1 | 1 | [‘email’, ‘phone’, ‘facebook’, ‘reviews’, ‘kba’] | t | t | New York, NY, United States | East Harlem | East Harlem | Manhattan | New York | NY | 10029 | New York | New York, NY | US | United States | 40.79851 | -73.94399 | t | Apartment | Entire home/apt | 1 | 1 | NA | 1 | Real Bed | {Internet,Wifi,“Air conditioning”,Kitchen,Elevator,“Free street parking”,“Buzzer/wireless intercom”,Heating,Washer,Dryer,“Smoke detector”,“Carbon monoxide detector”,Essentials,Shampoo,“Hair dryer”,“Hot water”,“Host greets you”} | NA | $80.00 | $600.00 | $1,600.00 | $100.00 | $80.00 | 1 | $20.00 | 10 | 120 | 10 | 10 | 120 | 120 | 10 | 120 | 3 months ago | t | 0 | 0 | 0 | 0 | 2019-07-08 | 9 | 4 | 2012-03-20 | 2018-11-19 | 93 | 10 | 9 | 10 | 10 | 9 | 10 | f | f | f | strict_14_with_grace_period | t | t | 1 | 1 | 0 | 0 | 0.10 | ||||||||
| 5099 | https://www.airbnb.com/rooms/5099 | 2.019071e+13 | 2019-07-08 | Large Cozy 1 BR Apartment In Midtown East | My large 1 bedroom apartment is true New York City living. The apt is in midtown on the east side and centrally located, just a 10-minute walk from Grand Central Station, Empire State Building, Times Square. The kitchen and living room are large and bright with Apple TV. I have a new Queen Bed that sleeps 2 people, and a Queen Aero Bed that can sleep 2 people in the living room. The apartment is located on the 5th floor of a walk up - no elevator (lift). | I have a large 1 bedroom apartment centrally located in Midtown East. A 10 minute walk from Grand Central Station, Times Square, Empire State Building and all major subway and bus lines. The apartment is located on the 5th floor of a pre-war walk up building-no elevator/lift. The apartment is bright with has high ceilings and flow through rooms. A spacious, cozy living room with Netflix and Apple TV. A large bright kitchen to sit and enjoy coffee or tea. The bedroom is spacious with a comfortable queen size bed that sleeps 2. I have a comfortable queen size aero bed that fits in the living room and sleeps 2. It can be tucked away for living space and opened when ready for bed. I’d be happy to give you tips and advice on the best ways to experience the most of NYC. The apartment’s location is great for sightseeing. ** Check out my listing guidebook ** If you would like to stay local in the area, there is a very long & famous strip of bars and restaurants along 3rd Avenue, which | My large 1 bedroom apartment is true New York City living. The apt is in midtown on the east side and centrally located, just a 10-minute walk from Grand Central Station, Empire State Building, Times Square. The kitchen and living room are large and bright with Apple TV. I have a new Queen Bed that sleeps 2 people, and a Queen Aero Bed that can sleep 2 people in the living room. The apartment is located on the 5th floor of a walk up - no elevator (lift). I have a large 1 bedroom apartment centrally located in Midtown East. A 10 minute walk from Grand Central Station, Times Square, Empire State Building and all major subway and bus lines. The apartment is located on the 5th floor of a pre-war walk up building-no elevator/lift. The apartment is bright with has high ceilings and flow through rooms. A spacious, cozy living room with Netflix and Apple TV. A large bright kitchen to sit and enjoy coffee or tea. The bedroom is spacious with a comfortable queen size bed that sleeps 2. I | none | My neighborhood in Midtown East is called Murray Hill. The area is very centrally located with easy access to explore . The apartment is about 5 blocks (7 minute walk) to the United Nations and Grand Central Station the main and most historic train station. Grand Central will give you access to every train in the city. The apartment is also very close to main attractions, It’s about a 10 minute walk to both the Empire State Building and Times Square. There’s a great shopping area with dozens of stores including H&M, Zara, The Gap, BeBe and the world famous Macy’s department store. These shops are a 10 minute walk up East 34th Street from 5th avenue and 8th avenue. If you would like to stay local in the area, there is a very long & famous strip of bars and restaurants along 3rd avenue, which is just around the corner from the apartment. It’s commonly known as the 3rd avenue strip. | Read My Full Listing For All Information. New York City really is the city that doesn’t sleep. There’s a constant flow of people, bikes and cars. The city can be noisy at times, if you’re a light sleeper, ear plugs would help. Check out my local guide book for things to do. | From the apartment is a 10 minute walk to Grand Central Station on East 42nd Street, a 10 minute walk to the Empire State Building on East 34th Street and 5th Avenue, a 10 minute walk to Times Square on West 42 Street and about 20 minutes walk to Central Park on 59th Street. Depending on how long you are staying, I would recommend a 7 day unlimited metro card. This allows you to travel unlimited all day and night on any train or bus in and outside of the city. Grand Central Station is the main NYC train station. You can find any train connection and get anywhere in Manhattan from Grand Central Station. You can get to Brooklyn, Queens, The Bronx and Staten Island from Grand Central Station The M15 bus is around the corner on 2nd avenue. This bus will take you from uptown Harlem to the East Village and South Street Seaport. It will also take you to the Staten Island ferry and Statue of Liberty and everywhere in between along the east side. The M101 bus is just up the street on 3rd aven | I will meet you upon arrival. | I usually check in with guests via text or email. I’m available by text, email or phone call with any questions, suggestions or to help out. | • Check-in time is 2PM. • Check-out time is 12 PM. Please be respectful of the space and leave the apartment in the condition you were welcomed into. | NA | NA | https://a0.muscache.com/im/pictures/0790b1a5-8981-41cc-a370-fa2b982a8803.jpg?aki_policy=large | NA | 7322 | https://www.airbnb.com/users/show/7322 | Chris | 2009-02-02 | New York, New York, United States | I’m an artist, writer, traveler, and a native new yorker. Welcome to my city. | within an hour | 100% | N/A | f | https://a0.muscache.com/im/pictures/user/26745d24-d818-4bf5-8f9e-26b097121ba7.jpg?aki_policy=profile_small | https://a0.muscache.com/im/pictures/user/26745d24-d818-4bf5-8f9e-26b097121ba7.jpg?aki_policy=profile_x_medium | Flatiron District | 1 | 1 | [‘email’, ‘phone’, ‘reviews’, ‘jumio’, ‘government_id’] | t | f | New York, NY, United States | Midtown East | Murray Hill | Manhattan | New York | NY | 10016 | New York | New York, NY | US | United States | 40.74767 | -73.97500 | f | Apartment | Entire home/apt | 2 | 1 | 1 | 1 | Real Bed | {TV,“Cable TV”,Internet,Wifi,Kitchen,“Buzzer/wireless intercom”,Heating,“Smoke detector”,“Carbon monoxide detector”,“Fire extinguisher”,Essentials,Shampoo,Hangers,“Hair dryer”,Iron,“Laptop friendly workspace”,“translation missing: en.hosting_amenity_49”,“translation missing: en.hosting_amenity_50”,“Hot water”,“Bed linens”,“Extra pillows and blankets”,“Host greets you”} | NA | $200.00 | $300.00 | $125.00 | 2 | $100.00 | 3 | 21 | 3 | 3 | 21 | 21 | 3 | 21 | 3 weeks ago | t | 23 | 48 | 48 | 129 | 2019-07-08 | 74 | 9 | 2009-04-20 | 2019-06-22 | 89 | 10 | 9 | 10 | 10 | 9 | 9 | f | f | f | strict_14_with_grace_period | t | t | 1 | 1 | 0 | 0 | 0.59 |
Please note only 6 rows have been shown below with head function
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## $ thumbnail_url : logi NA NA NA NA NA NA ...
## $ medium_url : logi NA NA NA NA NA NA ...
## $ picture_url : Factor w/ 48272 levels "https://a0.muscache.com/im/pictures/00027b83-c89a-4a76-ac36-de9ab57e878e.jpg?aki_policy=large",..: 11889 45757 26944 43799 48081 1209 6720 3977 2921 17476 ...
## $ xl_picture_url : logi NA NA NA NA NA NA ...
## $ host_id : int 2787 2845 4632 4869 7192 7322 7356 8967 7490 7549 ...
## $ host_url : Factor w/ 37457 levels "https://www.airbnb.com/users/show/1000014",..: 19513 19756 26531 27259 32826 33085 33159 35905 33430 33539 ...
## $ host_name : Factor w/ 11453 levels "","'Cil","-TheQueensCornerLot",..: 5051 4846 2962 6264 5982 1970 3601 9699 6935 1264 ...
## $ host_since : Factor w/ 3587 levels "","2008-08-22",..: 4 5 14 15 19 20 21 31 22 24 ...
## $ host_location : Factor w/ 1571 levels ""," Brooklyn, NY ",..: 982 982 982 982 886 982 982 982 982 982 ...
## $ host_about : Factor w/ 22144 levels "","'Eric is a director-actor-writer-producer-teacher of theatre; he also writes songs, makes bread, and advocates "| __truncated__,..: 3047 751 17091 16419 13912 9517 107 15173 21518 14390 ...
## $ host_response_time : Factor w/ 6 levels "","a few days or more",..: 6 5 4 5 3 6 3 5 3 5 ...
## $ host_response_rate : Factor w/ 87 levels "","0%","10%",..: 4 74 4 80 87 4 87 77 87 4 ...
## $ host_acceptance_rate : Factor w/ 2 levels "","N/A": 2 2 2 2 2 2 2 2 2 2 ...
## $ host_is_superhost : Factor w/ 3 levels "","f","t": 2 2 2 2 2 2 2 2 2 3 ...
## $ host_thumbnail_url : Factor w/ 37360 levels "","https://a0.muscache.com/defaults/user_pic-50x50.png?v=3",..: 6697 31587 34728 34835 36082 14713 5717 36888 36252 21825 ...
## $ host_picture_url : Factor w/ 37360 levels "","https://a0.muscache.com/defaults/user_pic-225x225.png?v=3",..: 6697 31587 34728 34835 36082 14713 5717 36888 36252 21825 ...
## $ host_neighbourhood : Factor w/ 416 levels "","Ã\230sterbro",..: 156 241 167 75 113 129 27 169 375 218 ...
## $ host_listings_count : int 6 5 1 1 1 1 1 1 1 4 ...
## $ host_total_listings_count : int 6 5 1 1 1 1 1 1 1 4 ...
## $ host_verifications : Factor w/ 536 levels "['email', 'facebook', 'google', 'reviews', 'offline_government_id', 'selfie', 'government_id', 'identity_manual']",..: 351 350 182 351 118 317 138 155 33 301 ...
## $ host_has_profile_pic : Factor w/ 3 levels "","f","t": 3 3 3 3 3 3 3 3 3 3 ...
## $ host_identity_verified : Factor w/ 3 levels "","f","t": 3 3 3 3 3 2 2 2 3 3 ...
## $ street : Factor w/ 315 levels " Astoria, NY, United States",..: 46 203 203 64 203 203 64 203 203 203 ...
## $ neighbourhood : Factor w/ 195 levels "","Allerton",..: 20 106 83 20 53 117 20 106 178 105 ...
## $ neighbourhood_cleansed : Factor w/ 221 levels "Allerton","Arden Heights",..: 109 128 95 42 62 138 14 96 203 36 ...
## $ neighbourhood_group_cleansed : Factor w/ 5 levels "Bronx","Brooklyn",..: 2 3 3 2 3 3 2 3 3 3 ...
## $ city : Factor w/ 306 levels ""," Astoria",..: 49 187 187 46 187 187 46 187 187 187 ...
## $ state : Factor w/ 8 levels "","CA","MP","New York ",..: 8 8 8 8 8 8 8 8 8 8 ...
## $ zipcode : Factor w/ 200 levels "","07093","07302",..: 123 20 29 142 31 18 121 21 27 5 ...
## $ market : Factor w/ 18 levels "","Adirondacks",..: 15 15 15 15 15 15 15 15 15 15 ...
## $ smart_location : Factor w/ 317 levels " Astoria, NY",..: 46 205 205 62 205 205 62 205 205 205 ...
## $ country_code : Factor w/ 1 level "US": 1 1 1 1 1 1 1 1 1 1 ...
## $ country : Factor w/ 1 level "United States": 1 1 1 1 1 1 1 1 1 1 ...
## $ latitude : num 40.6 40.8 40.8 40.7 40.8 ...
## $ longitude : num -74 -74 -73.9 -74 -73.9 ...
## $ is_location_exact : Factor w/ 2 levels "f","t": 1 1 2 2 2 1 1 2 2 2 ...
## $ property_type : Factor w/ 36 levels "Aparthotel","Apartment",..: 2 2 2 19 2 2 2 2 2 2 ...
## $ room_type : Factor w/ 3 levels "Entire home/apt",..: 2 1 2 1 1 1 2 2 2 1 ...
## $ accommodates : int 2 2 2 3 1 2 2 2 1 3 ...
## $ bathrooms : num 1 1 1 1 1 1 NA 1 1 1 ...
## $ bedrooms : int 1 0 1 1 NA 1 1 1 1 1 ...
## $ beds : int 1 1 1 4 1 1 1 1 1 2 ...
## $ bed_type : Factor w/ 5 levels "Airbed","Couch",..: 5 5 4 5 5 5 3 5 5 5 ...
## $ amenities : Factor w/ 44292 levels "{\"Air conditioning\",\"Carbon monoxide detector\",\"First aid kit\",\"Lock on bedroom door\",\"translation mis"| __truncated__,..: 9863 23810 339 7790 3243 9905 38040 20063 920 11136 ...
## $ square_feet : int NA NA NA 500 NA NA NA NA NA NA ...
## $ price : Factor w/ 674 levels "$0.00","$1,000.00",..: 111 216 113 642 619 189 551 612 612 113 ...
## $ weekly_price : Factor w/ 599 levels "","$1,000.00",..: 260 162 1 426 440 1 1 366 1 1 ...
## $ monthly_price : Factor w/ 681 levels "","$1,000.00",..: 681 1 1 233 131 1 1 1 1 1 ...
## $ security_deposit : Factor w/ 225 levels "","$0.00","$1,000.00",..: 32 148 95 178 32 137 169 1 1 2 ...
## $ cleaning_fee : Factor w/ 207 levels "","$0.00","$1,000.00",..: 91 5 182 1 188 26 2 43 1 128 ...
## $ guests_included : int 1 2 2 1 1 2 1 1 1 2 ...
## $ extra_people : Factor w/ 104 levels "$0.00","$10.00",..: 51 1 29 1 29 3 45 8 1 2 ...
## $ minimum_nights : int 1 1 3 1 10 3 45 2 2 1 ...
## $ maximum_nights : int 730 1125 7 730 120 21 730 14 14 1125 ...
## $ minimum_minimum_nights : int 1 1 3 1 10 3 45 2 2 1 ...
## $ maximum_minimum_nights : int 1 1 3 1 10 3 45 2 2 1 ...
## $ minimum_maximum_nights : int 730 1125 7 730 120 21 730 14 14 1125 ...
## $ maximum_maximum_nights : int 730 1125 7 730 120 21 730 14 14 1125 ...
## $ minimum_nights_avg_ntm : num 1 1 3 1 10 3 45 2 2 1 ...
## $ maximum_nights_avg_ntm : num 730 1125 7 730 120 ...
## $ calendar_updated : Factor w/ 92 levels "1 week ago","10 months ago",..: 27 38 32 91 26 27 10 39 19 91 ...
## $ has_availability : Factor w/ 1 level "t": 1 1 1 1 1 1 1 1 1 1 ...
## $ availability_30 : int 30 25 30 0 0 23 0 3 0 0 ...
## $ availability_60 : int 60 55 60 0 0 48 0 10 0 0 ...
## $ availability_90 : int 90 80 90 3 0 48 0 12 0 0 ...
## $ availability_365 : int 365 355 365 194 0 129 0 220 0 188 ...
## $ calendar_last_scraped : Factor w/ 2 levels "2019-07-08","2019-07-09": 2 2 1 2 1 1 2 1 1 1 ...
## $ number_of_reviews : int 9 45 0 270 9 74 49 430 118 160 ...
## $ number_of_reviews_ltm : int 2 11 0 69 4 9 0 43 0 12 ...
## $ first_review : Factor w/ 2898 levels "","2009-03-12",..: 1587 23 1 1162 357 3 9 5 15 14 ...
## $ last_review : Factor w/ 1765 levels "","2011-03-28",..: 1503 1717 1 1762 1534 1749 1124 1751 1048 1736 ...
## $ review_scores_rating : int 98 95 NA 90 93 89 90 84 98 94 ...
## $ review_scores_accuracy : int 10 10 NA 10 10 10 8 9 10 10 ...
## $ review_scores_cleanliness : int 10 9 NA 9 9 9 8 7 10 9 ...
## $ review_scores_checkin : int 10 10 NA 10 10 10 10 10 10 10 ...
## $ review_scores_communication : int 10 10 NA 10 10 10 10 9 10 10 ...
## $ review_scores_location : int 10 10 NA 10 9 9 9 10 10 9 ...
## $ review_scores_value : int 10 9 NA 9 10 9 9 9 10 9 ...
## $ requires_license : Factor w/ 1 level "f": 1 1 1 1 1 1 1 1 1 1 ...
## $ license : Factor w/ 10 levels "","11-3461723",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ jurisdiction_names : Factor w/ 6 levels "","{\"Connecticut State\"}",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ instant_bookable : Factor w/ 2 levels "f","t": 1 1 1 1 1 1 1 1 1 1 ...
## $ is_business_travel_ready : Factor w/ 1 level "f": 1 1 1 1 1 1 1 1 1 1 ...
## $ cancellation_policy : Factor w/ 6 levels "flexible","moderate",..: 2 4 4 2 4 4 4 4 1 4 ...
## [list output truncated]Missing Values by Attribute
#Check Missing values in each column
colSums(is.na(rentAirbnbListings))## id
## 0
## listing_url
## 0
## scrape_id
## 0
## last_scraped
## 0
## name
## 0
## summary
## 0
## space
## 0
## description
## 0
## experiences_offered
## 0
## neighborhood_overview
## 0
## notes
## 0
## transit
## 0
## access
## 0
## interaction
## 0
## house_rules
## 0
## thumbnail_url
## 48895
## medium_url
## 48895
## picture_url
## 0
## xl_picture_url
## 48895
## host_id
## 0
## host_url
## 0
## host_name
## 0
## host_since
## 0
## host_location
## 0
## host_about
## 0
## host_response_time
## 0
## host_response_rate
## 0
## host_acceptance_rate
## 0
## host_is_superhost
## 0
## host_thumbnail_url
## 0
## host_picture_url
## 0
## host_neighbourhood
## 0
## host_listings_count
## 21
## host_total_listings_count
## 21
## host_verifications
## 0
## host_has_profile_pic
## 0
## host_identity_verified
## 0
## street
## 0
## neighbourhood
## 0
## neighbourhood_cleansed
## 0
## neighbourhood_group_cleansed
## 0
## city
## 0
## state
## 0
## zipcode
## 0
## market
## 0
## smart_location
## 0
## country_code
## 0
## country
## 0
## latitude
## 0
## longitude
## 0
## is_location_exact
## 0
## property_type
## 0
## room_type
## 0
## accommodates
## 0
## bathrooms
## 56
## bedrooms
## 22
## beds
## 40
## bed_type
## 0
## amenities
## 0
## square_feet
## 48487
## price
## 0
## weekly_price
## 0
## monthly_price
## 0
## security_deposit
## 0
## cleaning_fee
## 0
## guests_included
## 0
## extra_people
## 0
## minimum_nights
## 0
## maximum_nights
## 0
## minimum_minimum_nights
## 0
## maximum_minimum_nights
## 0
## minimum_maximum_nights
## 0
## maximum_maximum_nights
## 0
## minimum_nights_avg_ntm
## 0
## maximum_nights_avg_ntm
## 0
## calendar_updated
## 0
## has_availability
## 0
## availability_30
## 0
## availability_60
## 0
## availability_90
## 0
## availability_365
## 0
## calendar_last_scraped
## 0
## number_of_reviews
## 0
## number_of_reviews_ltm
## 0
## first_review
## 0
## last_review
## 0
## review_scores_rating
## 11022
## review_scores_accuracy
## 11060
## review_scores_cleanliness
## 11043
## review_scores_checkin
## 11078
## review_scores_communication
## 11055
## review_scores_location
## 11082
## review_scores_value
## 11080
## requires_license
## 0
## license
## 0
## jurisdiction_names
## 0
## instant_bookable
## 0
## is_business_travel_ready
## 0
## cancellation_policy
## 0
## require_guest_profile_picture
## 0
## require_guest_phone_verification
## 0
## calculated_host_listings_count
## 0
## calculated_host_listings_count_entire_homes
## 0
## calculated_host_listings_count_private_rooms
## 0
## calculated_host_listings_count_shared_rooms
## 0
## reviews_per_month
## 10052Observations
- The dimension of Airbnb Cost data is 48895, 106.
- Table structure: Data is in the Long form, which is convenient for our EDA. Money columns need to be changed to numeric.
- Slicing Relevant columns: There are 106 columns for various dimensions, only pick the relevant columns for our further analysis will be required.
- Filter Criteria: filter 2 bedroom listings from bedroom column.
- Missing Values: Many(282804) NA values can be observed in the dataset.
Scrubbing Methodology
- Check for NA Values in columns
- Filter Criteria: Filter out only 2 bedroom listings.
- Relevent columnns: Only column that are relevent will be sliced for further analysis
- Property data: zipcode,id, last_scraped, neighbourhood_group_cleansed, latitude,longitude,property_type,room_type,accommodates, bathrooms,bedrooms,beds,security_deposit, price, cleaning_fee
- Demand/Avaliablity data: availability_30,availability_60,availability_90,availability_365
- Review Data :number_of_reviews,first_review,last_review,review_scores_rating,reviews_per_month
- Change money columns to numeric
- Change first_review, last_review columns to date from factors
Below is the scrubbing function that is used for Listings data
listings_scrub <- function(rentAirbnbListings,NoOfBedrooms){
revenue_filter <- c('zipcode','id', 'last_scraped', 'neighbourhood_group_cleansed',
'latitude','longitude','property_type','room_type','accommodates',
'bathrooms','bedrooms','beds','security_deposit', 'price', 'cleaning_fee',
'availability_30','availability_60','availability_90','availability_365',
'number_of_reviews','first_review','last_review','review_scores_rating',
'reviews_per_month')
rentAirbnbListings_filt <-
rentAirbnbListings %>% # read house rent data
.[, colnames(.) %in% revenue_filter] # filter by subset of variables
convert_price_to_numeric <- function(data, var)
{
index <- which(colnames(data) == var) # indexes of price columns
df1 <- str_sub(data[,index], 2) # remove '$' character
df2 <- gsub(",","",df1) %>% # substitute ',' with blank
as.numeric() # convert to numeric
return(df2)
}
#Assumption: Cleaning fee and security deposit is considered as 0 where Blank
rentAirbnbListings_filt$price <- convert_price_to_numeric(rentAirbnbListings_filt,'price')
rentAirbnbListings_filt$security_deposit <- convert_price_to_numeric(rentAirbnbListings_filt,'security_deposit')
rentAirbnbListings_filt$cleaning_fee <- convert_price_to_numeric(rentAirbnbListings_filt, 'cleaning_fee')
#Calculate total rent amount (rental/night + cleaning fee)
rentAirbnbListings_filt<-rentAirbnbListings_filt%>%
mutate( totalRent=(price+ ifelse(is.na(cleaning_fee),as.numeric(0),cleaning_fee)))
#convert factor to date
rentAirbnbListings_filt$first_review<-as.Date(as.character(rentAirbnbListings_filt$first_review, format = "%m/%d/%Y"))
rentAirbnbListings_filt$last_review<-as.Date(as.character(rentAirbnbListings_filt$last_review, format = "%m/%d/%Y"))
rentAirbnbListings_filt<-rentAirbnbListings_filt%>%filter(bedrooms==NoOfBedrooms)
return(rentAirbnbListings_filt)
}
NoOfBedrooms<-2
listings_scbd<-listings_scrub(rentAirbnbListings,NoOfBedrooms)
dimListPostScrb<-dim(listings_scbd)# Dimension post scrubbing
dimListPostScrb## [1] 6497 25Dimension post scrubbing: 6497, 25
Airbnb data post scrubbing
kable(head(listings_scbd)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| id | last_scraped | neighbourhood_group_cleansed | zipcode | latitude | longitude | property_type | room_type | accommodates | bathrooms | bedrooms | beds | price | security_deposit | cleaning_fee | availability_30 | availability_60 | availability_90 | availability_365 | number_of_reviews | first_review | last_review | review_scores_rating | reviews_per_month | totalRent |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 7750 | 2019-07-08 | Manhattan | 10029 | 40.79685 | -73.94872 | Apartment | Entire home/apt | 4 | 1 | 2 | 2 | 190 | NA | NA | 4 | 14 | 14 | 249 | 0 | NA | NA | NA | NA | 190 |
| 13050 | 2019-07-09 | Brooklyn | 11221 | 40.68554 | -73.94090 | Townhouse | Entire home/apt | 5 | 1 | 2 | 2 | 115 | 150 | 85 | 0 | 0 | 0 | 0 | 11 | 2010-04-18 | 2017-01-01 | 94 | 0.10 | 200 |
| 14290 | 2019-07-08 | Brooklyn | 11206 | 40.70420 | -73.93560 | Loft | Entire home/apt | 2 | 1 | 2 | 0 | 228 | 350 | 128 | 14 | 20 | 50 | 140 | 82 | 2009-12-01 | 2019-05-17 | 94 | 0.70 | 356 |
| 15396 | 2019-07-08 | Manhattan | 10001 | 40.74623 | -73.99530 | Apartment | Entire home/apt | 4 | 2 | 2 | 2 | 375 | 1000 | 120 | 30 | 60 | 90 | 180 | 5 | 2016-03-23 | 2018-11-03 | 100 | 0.12 | 495 |
| 15711 | 2019-07-08 | Manhattan | 10162 | 40.77065 | -73.95269 | Apartment | Entire home/apt | 6 | 1 | 2 | 4 | 250 | 500 | 200 | 0 | 12 | 28 | 231 | 66 | 2010-01-06 | 2019-03-30 | 93 | 0.57 | 450 |
| 16458 | 2019-07-08 | Brooklyn | 11215 | 40.67343 | -73.98338 | Condominium | Entire home/apt | 4 | 2 | 2 | 1 | 225 | NA | NA | 0 | 0 | 0 | 0 | 4 | 2017-07-02 | 2017-09-24 | 100 | 0.16 | 225 |
Please note only 6 rows have been shown above with head function
3.3 Data Merge
Merge (Inner join) Zillow cost and Airbnb rent data based on Zipcode
RentCostMerged<-merge(listings_scbd,zlow_scbd,by.x="zipcode",by.y="zipcode")#merge the data
NoOfRentCost<-dim(RentCostMerged)#calculate dimensions
rowsRentCost<-nrow(RentCostMerged)#No of rows
colsRentCost<-ncol(RentCostMerged)#No of cols
NAvaluesRentCost<-sum(is.na(RentCostMerged))# No of missing data
NoOfRentCost #Dimension post Merge## [1] 1565 26Dimension post Merge: 1565, 26
Merged Airbnb Rental & Zillow Cost data
kable(head(RentCostMerged)) %>% kable_styling(bootstrap_options = c("striped", "hover", "responsive")) %>% scroll_box(width = "100%", height = "250px")| zipcode | id | last_scraped | neighbourhood_group_cleansed | latitude | longitude | property_type | room_type | accommodates | bathrooms | bedrooms | beds | price | security_deposit | cleaning_fee | availability_30 | availability_60 | availability_90 | availability_365 | number_of_reviews | first_review | last_review | review_scores_rating | reviews_per_month | totalRent | currentPrice |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10003 | 19288937 | 2019-07-08 | Manhattan | 40.72784 | -73.98883 | Apartment | Entire home/apt | 4 | 1.0 | 2 | 2 | 250 | 500 | 110 | 1 | 2 | 10 | 176 | 99 | 2017-07-02 | 2019-06-21 | 97 | 4.03 | 360 | 2336587 |
| 10003 | 13866112 | 2019-07-08 | Manhattan | 40.72394 | -73.98872 | Apartment | Entire home/apt | 2 | 1.5 | 2 | 1 | 165 | 250 | 85 | 0 | 0 | 0 | 0 | 3 | 2016-07-31 | 2017-12-09 | 100 | 0.08 | 250 | 2336587 |
| 10003 | 31835260 | 2019-07-08 | Manhattan | 40.73782 | -73.98547 | Apartment | Entire home/apt | 5 | 1.0 | 2 | 2 | 218 | 0 | 120 | 0 | 0 | 0 | 0 | 6 | 2019-03-10 | 2019-05-12 | 93 | 1.49 | 338 | 2336587 |
| 10003 | 5207973 | 2019-07-08 | Manhattan | 40.72950 | -73.98623 | Apartment | Entire home/apt | 4 | 1.0 | 2 | 2 | 230 | 500 | 60 | 0 | 0 | 0 | 0 | 1 | 2015-03-31 | 2015-03-31 | NA | 0.02 | 290 | 2336587 |
| 10003 | 17843998 | 2019-07-08 | Manhattan | 40.72571 | -73.98789 | Apartment | Entire home/apt | 4 | 1.0 | 2 | 2 | 250 | 200 | 75 | 2 | 5 | 16 | 264 | 8 | 2017-06-14 | 2019-06-02 | 88 | 0.32 | 325 | 2336587 |
| 10003 | 4126452 | 2019-07-08 | Manhattan | 40.73327 | -73.98503 | Apartment | Entire home/apt | 3 | 1.0 | 2 | 2 | 200 | 300 | 125 | 0 | 0 | 0 | 157 | 13 | 2014-10-06 | 2015-10-19 | 80 | 0.22 | 325 | 2336587 |
Please note only 6 rows have been shown above with head function
Observation
- The dimension of the final dataset is 1565, 26. These observations would be used for Exploratory Data Analysis.
- Rows have reduced from 48,895 and 8946 rows in Airbnb Listings & Zillow cost files to 1565 rows in the final dataset.
- Columns have also reduced from 106 & 262 in Airbnb Listings & Zillow cost files, to 26 columns in the final dataset.
- A total of 2123 NA values are observed in the final RentCostMerged dataset.
We need to further do a data quality check for NA and Abnormal Values in the merged data. We will handle the data quality checks in the Exploratory Data Analysis phase.
4.0 Exploratory Data Analysis
Exploratory Data Analysis (EDA) is usually the first step of the data analysis phase. In statistics, this is an approach to analyze data sets to summarize their main characteristics.
Below we use statistical & Visualization techniques to draw some key insights from the data
4.1 Data Quality Check
Data Quality check is an important phase of EDA. As the quality of data at hand and heavily influence the accuracy of data analysis, this is taken as the primary step to start with…
4.1.1 Summary Statistics
summary(RentCostMerged)## zipcode id last_scraped
## 11215 :189 Min. : 16458 2019-07-08:1090
## 10036 :147 1st Qu.: 7664343 2019-07-09: 475
## 10003 :136 Median :17586014
## 10025 :124 Mean :17837441
## 11217 :124 3rd Qu.:28501387
## 10011 :106 Max. :36477307
## (Other):739
## neighbourhood_group_cleansed latitude longitude
## Bronx : 0 Min. :40.52 Min. :-74.21
## Brooklyn : 501 1st Qu.:40.68 1st Qu.:-74.00
## Manhattan :1022 Median :40.73 Median :-73.99
## Queens : 16 Mean :40.73 Mean :-73.98
## Staten Island: 26 3rd Qu.:40.76 3rd Qu.:-73.97
## Max. :40.81 Max. :-73.76
##
## property_type room_type accommodates
## Apartment :1290 Entire home/apt:1465 Min. : 1.000
## Condominium : 68 Private room : 100 1st Qu.: 4.000
## House : 57 Shared room : 0 Median : 4.000
## Loft : 55 Mean : 4.458
## Townhouse : 44 3rd Qu.: 5.000
## Serviced apartment: 35 Max. :16.000
## (Other) : 16
## bathrooms bedrooms beds price
## Min. :0.000 Min. :2 Min. :0.000 Min. : 50.0
## 1st Qu.:1.000 1st Qu.:2 1st Qu.:2.000 1st Qu.: 165.0
## Median :1.000 Median :2 Median :2.000 Median : 228.0
## Mean :1.299 Mean :2 Mean :2.381 Mean : 284.6
## 3rd Qu.:2.000 3rd Qu.:2 3rd Qu.:3.000 3rd Qu.: 320.0
## Max. :3.500 Max. :2 Max. :6.000 Max. :4000.0
## NA's :3
## security_deposit cleaning_fee availability_30 availability_60
## Min. : 0.0 Min. : 0.0 Min. : 0.000 Min. : 0.00
## 1st Qu.: 0.0 1st Qu.: 75.0 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 250.0 Median :100.0 Median : 0.000 Median : 7.00
## Mean : 503.8 Mean :109.9 Mean : 5.894 Mean :15.87
## 3rd Qu.: 500.0 3rd Qu.:150.0 3rd Qu.: 8.000 3rd Qu.:27.00
## Max. :5000.0 Max. :482.0 Max. :30.000 Max. :60.00
## NA's :420 NA's :212
## availability_90 availability_365 number_of_reviews first_review
## Min. : 0.00 Min. : 0.0 Min. : 0.00 Min. :2010-05-20
## 1st Qu.: 0.00 1st Qu.: 0.0 1st Qu.: 1.00 1st Qu.:2015-10-13
## Median :12.00 Median : 59.0 Median : 4.00 Median :2017-05-21
## Mean :25.79 Mean :120.7 Mean : 19.78 Mean :2017-01-13
## 3rd Qu.:49.00 3rd Qu.:247.0 3rd Qu.: 17.00 3rd Qu.:2018-09-02
## Max. :90.00 Max. :365.0 Max. :403.00 Max. :2019-07-07
## NA's :367
## last_review review_scores_rating reviews_per_month
## Min. :2012-10-02 Min. : 20.00 Min. : 0.010
## 1st Qu.:2018-05-04 1st Qu.: 92.00 1st Qu.: 0.160
## Median :2019-04-29 Median : 96.00 Median : 0.470
## Mean :2018-08-31 Mean : 94.15 Mean : 1.187
## 3rd Qu.:2019-06-23 3rd Qu.:100.00 3rd Qu.: 1.700
## Max. :2019-07-08 Max. :100.00 Max. :12.050
## NA's :367 NA's :387 NA's :367
## totalRent currentPrice
## Min. : 50.0 Min. : 413626
## 1st Qu.: 240.0 1st Qu.:1628300
## Median : 325.0 Median :1910420
## Mean : 379.6 Mean :2156909
## 3rd Qu.: 450.0 3rd Qu.:2897002
## Max. :4040.0 Max. :3713170
## This gives a quick overview of data structure
Observation
- Missing values are present in few columns, namely security_deposit,Cleaning_fee,reviews_per_month,last_review,first_review,review_scores_rating,bathrooms.
- The rest structure looks fine to proceed.
4.1.2 Missing Values
Often, data can have missing values due to a variety of reasons, for example with listings data, some observations may not have been provided. It is important for us to analyze our data, and get a sense of what the missing values are so we can decide how we want to handle missing values for our Analysis.
MissingValues<-as.vector(sapply(RentCostMerged, function(x) sum(is.na(x))))
df_MissingValues<-data.frame(as.character(colnames(RentCostMerged)),MissingValues)
#Coulmns that have Missing values
df_MissingValues<-
df_MissingValues%>%
rename(Attributes="as.character.colnames.RentCostMerged..")%>%
filter(MissingValues!=0)%>%
arrange(desc(MissingValues))
df_MissingValues## Attributes MissingValues
## 1 security_deposit 420
## 2 review_scores_rating 387
## 3 first_review 367
## 4 last_review 367
## 5 reviews_per_month 367
## 6 cleaning_fee 212
## 7 bathrooms 3Plot indicating the amount of missing values in Merged dataset
mv<-ggplot(df_MissingValues, aes(x=Attributes, y=MissingValues)) +
geom_bar(stat = "identity",fill="#3990E5", alpha=.6, width=.5)+
geom_text(aes(label=MissingValues), hjust=-1.6, size=3.5)+
coord_flip()+
theme(text = element_text(size=10))+
ggtitle("Missing Values")
ggplotly(mv)- Security Deposit: 420 properties have Missing Security deposit. This can be because the owners did not demand this amount from the tenant, hence the column has blanks. We assume that blank/NA indicates ‘0’ amount.
- Cleaning_fee: 212 properties do not have any cleaning fee included. We assume that blank/NA indicates ‘0’ amount.
- Review per month: Missing values in this column indicate that 367 properties did not have any review ever. We assume that blank/NA indicates ‘0’ Reviews given.
- last_review/first_review: 367 properties have no first or last review. These properties have never been reviewed, last and first have an equal number of blanks.
- Review score rating: 387 Missing values in this column indicate that 20 properties(387-367=20) did not have any rating yet but did have review/reviews. We assume that blank/NA indicates ‘0’ ratings given.
Missing values in these columns make sense as some are “fee” columns that may have not been demanded by the owner or Review columns which may be blank because the listing never got any review yet. It is safe to assume the missing values as 0 here.
4.1.3 Total properties by Neighbourhood
Below plot depicts the number of total properties segregated by Neighbourhood
p1<-ggplot(RentCostMerged, aes(neighbourhood_group_cleansed)) +
geom_bar(fill='#3990E5' ,alpha=0.5) +
labs(x = "Neighbourhood",
y = "Number of Properties",
title = "Total properties",
subtitle = "Segrated by Neighbourhood") +
geom_text(stat='count', aes(label=..count..),vjust=-0.6)
ggplotly(p1)- Manhattan has a maximum number of properties: 1022
- Brooklyn has close to half as many as in Manhattan : 500
- Queens & Staten Island have very few as compared to Manhattan & Brooklyn : 16 & 26 respectively.
Below plot depicts the number of total properties segregated by Neighbourhood & zipcode
p2<-ggplot(RentCostMerged, aes(zipcode, fill = neighbourhood_group_cleansed)) +
geom_bar(alpha=0.5) +
geom_text(stat='count', aes(label=..count..), vjust= -0.3) +
guides(fill = guide_legend(title = "Neighbourhood"))+
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1),
plot.caption = element_text(hjust = 0, face = "italic"),
legend.position="bottom"
) +
labs(x = "Zipcode",
y = "Number of Properties",
title = "Segrated by Neighbourhood & zipcode",
caption = "Data source: Airbnb & Zillow")
p2 
Observation
- A property in zipcode 10013 has been incorrectly labeled as under Brooklyn. Removing this abnormal property will be better for analysis.
- Some zip codes have a-few listing only, as compared to others. Keeping these zip codes in our analysis can result in skewed output drastically. Hence we need to further analyze this..
A Deeper analysis of Number of listings in each zipcode
B1<-RentCostMerged %>%
group_by(zipcode) %>%
summarise(ListingCount=n())
# 25TH Percentile of total listings by zipcode
per25<-quantile(B1$ListingCount,.25)
per25 # 25TH Percentile of total listings by zipcode## 25%
## 7.75Observation
- 25th percentile= 7.75 indicates there 25% zipcodes have less than 8 listings.
- A boxplot can help us understand the distribution of total listings in each zipcode.
B2<-RentCostMerged %>%
group_by(zipcode) %>%
summarise(ListingCount=n())%>%
filter(ListingCount>=10)
B11<-ggplot(B1,aes(x="",y=ListingCount))+
geom_boxplot(
# custom boxes
color="blue",
fill="blue",
alpha=0.2,
# Notch?
notch=TRUE,
notchwidth = 0.5,
# custom outliers
outlier.colour="red",
outlier.fill="red",
outlier.size=3
)+
labs(y = "Listings",
x = "Zipcode",
title = "Pre removal",
subtitle = "of zips with <10 listings"
)
B22<-ggplot(B2,aes(x="",y=ListingCount))+
geom_boxplot(
# custom boxes
color="darkgreen",
fill="green",
alpha=0.3,
# Notch?
notch=TRUE,
notchwidth = 0.5,
# custom outliers
outlier.colour="red",
outlier.fill="red",
outlier.size=3
)+
labs(y = "Listings ",
x = "Zipcode",
title = "Post removal",
subtitle = "of zips with <10 listings"
)
grid.arrange(B11,B22, ncol = 2, nrow = 1)## notch went outside hinges. Try setting notch=FALSE.
Observation
25 percentile of zipcodes have 8 or less number of 2 bedroom properties listed in airbnb. Revenue analysis can get skewed when a zipcode has very few listings. For now, zipcodes with less than 10 two-bedroom airbnb listings are removed from the analysis
Correction
Based on above analysis corrections are made below
RentCostMerged<-subset(RentCostMerged,id!='22216259')
ReleventZips_num <-
RentCostMerged %>%
group_by(zipcode) %>%
summarise(ListingCount=n())%>%
filter(ListingCount>=10)%>%
arrange(desc(ListingCount))
RentCostMerged_final<-merge(RentCostMerged,ReleventZips_num,by.x="zipcode",by.y="zipcode")
#Updating the Review columns to 0 where there were "no review per month" & rating was never given
RentCostMerged_final$reviews_per_month<-ifelse(is.na(RentCostMerged_final$reviews_per_month)==TRUE,0,RentCostMerged_final$reviews_per_month)
RentCostMerged_final$review_scores_rating<-ifelse(is.na(RentCostMerged_final$review_scores_rating)==TRUE,as.numeric(0),RentCostMerged_final$review_scores_rating)
nrowfnl<-nrow(RentCostMerged_final)
ncolfnl<-ncol(RentCostMerged_final)
nrowfnl#Number of rows post correction## [1] 1541ncolfnl#Number of columns post correction## [1] 27We finally have 1541 properties to analyze based on 27 attributes
4.2 Data Analysis
Analysis based on visualization
EDA through Visualizations is helpful in understanding the hidden patterns in data that the human eye may miss to notice in tabular format.
Here, we used some statistical Visualization techniques to translate the elusive patterns and correlations in data.
What Zipcodes are better to invest ?
The analysis to understand what will be better than the other is based on the question “what is desired out of the investment”. For instance, If an investor wants a shorter break-even time in investment then he has to select based on that dimension.
Here, the analysis is based on 4 such dimensions, namely:
- Customer Preference
- Market Demand
- Customer Feedback/Reviews
- Break-Even Time
A deep dive Analysis on these individual dimensions in performed below..
4.2.1 Customer Preferance
There can be a plethora of reasons behind a prominent customer propensity for a locality such as Environment and social influence, Commercial implication, Proximity, etc. Hence, it goes without saying that a property that is located in an area that is more customer-preferred will flourish more and hence will be in demand and earn better returns on Investment.
Revisiting the same corrected graph
ReleventZips_num <-
RentCostMerged_final %>%
group_by(zipcode,neighbourhood_group_cleansed) %>%
summarise(ListingCount=n())%>%
arrange(desc(ListingCount))
p2<-ggplot(RentCostMerged_final, aes(zipcode, fill = neighbourhood_group_cleansed)) +
geom_bar(alpha=0.5) +
geom_text(stat='count', aes(label=..count..), vjust= -0.3) +
geom_hline(aes(yintercept = median(ReleventZips_num$ListingCount)),color="grey31",linetype="dashed", size=1)+
guides(fill = guide_legend(title = "Neighbourhood"))+
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1),
plot.subtitle = element_text(hjust = 0.5,size = 12),# Center subtitle
plot.caption = element_text(hjust = 0, face = "italic"),
legend.position="bottom"
) +
labs(x = "Zipcode",
y = "Number of Properties",
title = "Segrated by zipcode",
caption = "Data source: Airbnb & Zillow")
p2 
All above the median reference line are better Zipcodes for investment
Observation
It can be observed that most Airbnb rental properties are in the areas of Manhattan followed by Brooklyn and then Queens & Staten Island.
Hence, Making an Investment in a location that is mostly “preferred by customer” seems reasonable. There are high chances that investment will flourish as it looks like the best market for short-term rental investment considering the highest Number of Listings in the city!
Recommended Zipcodes based on Customer Preferance:
#Zipcode recommended:
aboverMedian_byPreferance<-subset(ReleventZips_num,ListingCount>=median(ReleventZips_num$ListingCount))
aboverMedian_byPreferance## # A tibble: 9 x 3
## # Groups: zipcode [9]
## zipcode neighbourhood_group_cleansed ListingCount
## <fct> <fct> <int>
## 1 11215 Brooklyn 189
## 2 10036 Manhattan 147
## 3 10003 Manhattan 136
## 4 10025 Manhattan 124
## 5 11217 Brooklyn 124
## 6 10011 Manhattan 106
## 7 10013 Manhattan 104
## 8 11231 Brooklyn 93
## 9 10014 Manhattan 904.2.2 Demand
Assumption: Avalilablity is inversely propotional to Demand
Demand can be an important parameter to evaluate before making any investment. High demand can be because of many factors such as location, sight & view, proximity, etc. Investing in such an area would result in heavy-booking of property which will improve the occupancy rate and in turn Revenue.
Here, to analyze the demand of properties in various zip codes availability is parameter is analyzed. This is based on the fact that a good apartment that is in high demand will have low Availablity
Correlation analysis of various availablity columns
C<-cor(RentCostMerged_final[,c(16,17,18,19,25,26)])
corrplot(C, method = "square",type="lower")
Observation
- We observe a strong correlation between availability parameters. This proves that any property that is not available(in high demand) in the next 30 days will possibly not be available in the next 365 days as well.
- We also observe a weak correlation between availability and rent. It can be inferred that high availability indicates low demand and in turn low rent.
- Rent and price are highly correlated.
For further analysis we take availablity_365 as an indicator of availablity
Below Visualization indicates the Zipcodes vs Demand
ReleventZips_bydmnd<-
RentCostMerged_final %>%
group_by(neighbourhood_group_cleansed,zipcode) %>%
summarise(demand=100-(100*(median(availability_365)))/365) %>%
arrange(desc(demand))
p3<-ggplot(ReleventZips_bydmnd,aes(x =zipcode,y= demand, fill = neighbourhood_group_cleansed )) +
geom_bar(position = 'dodge',stat = "identity",alpha=0.6) +
geom_text(aes(label=round(demand,2)),position=position_dodge(width=0.9), vjust=-0.25,size=3)+
geom_hline(aes(yintercept = median(ReleventZips_bydmnd$demand)),color="grey31",linetype="dashed", size=1)+
scale_y_continuous(labels = scales::comma) +
scale_colour_brewer(palette = "Pastel2") +
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1),
legend.position="bottom",
plot.caption = element_text(hjust = 0, face = "italic")
) +
guides(fill = guide_legend(title = "Neighbourhood")) +
labs(y = "Percentage Demand '%' ",
x = "Zipcode",
title = "Percentage Demand of Zipcodes",
subtitle = "Median values of each zipcode in used for calculation",
caption = "Data source: Airbnb & Zillow")
p3
All above the median reference line are better Zipcodes for investment
Observation
Close to half of zipodes of manhattan and all zipcodes from Brooklyn are in high demand, Brooklyn seems to be performing better here.
Recommended Zipcodes based on Occupancy/Demand
#Zipcode recommended:
aboverMedian_byDemand<-subset(ReleventZips_bydmnd,demand>=median(ReleventZips_bydmnd$demand))
aboverMedian_byDemand## # A tibble: 9 x 3
## # Groups: neighbourhood_group_cleansed [2]
## neighbourhood_group_cleansed zipcode demand
## <fct> <fct> <dbl>
## 1 Brooklyn 11217 97.4
## 2 Manhattan 10025 97.4
## 3 Manhattan 10014 95.9
## 4 Brooklyn 11231 94.0
## 5 Brooklyn 11201 93.4
## 6 Manhattan 10011 93.2
## 7 Manhattan 10021 91.9
## 8 Manhattan 10003 91.0
## 9 Brooklyn 11215 90.14.2.3 Customer Feedback or Reviews
Reviews not only have the power to influence consumer decisions but can strengthen a company’s credibility. Reviews have the power to gain customer trust, and they encourage people to interact with the company. Customer interaction ultimately leads to improved profits for businesses. Good reviews compel more customers to select a property and bad reviews deter them. Hence, this can be an important dimension to analyze.
Correlation analysis of various availablity columns
R<-cor(RentCostMerged_final[,c(19,20,23,24,25,26)])
corrplot(R, method = "square",type="lower")
Observation
- It is surprising to notice that number_of reviews,review_scores_rating & reviews_per_month have a weak association with Rent. This indicates good reviews of a locality do indicate higher rental income. But, it goes without saying that reviews do impact a huge customer populace. We can also observe the positive correlation between number_of_reviews & availablity_365. This indicates that a zipcode with better Reviews has better odds to be demand than the one that does not…
Below scatter plot show the better performing Zipcode in Review dimension
#relevent zips
ReleventZips_Rvu<-RentCostMerged_final %>%
group_by(neighbourhood_group_cleansed,zipcode) %>%
summarise(RvperMonth_avg=mean(number_of_reviews),RvScoreRating_med=(median(review_scores_rating))/10)%>% arrange(desc(RvScoreRating_med),desc(RvperMonth_avg))
m<-round(median(ReleventZips_Rvu$RvperMonth_avg),2)
r<-median(ReleventZips_Rvu$RvScoreRating_med)
numDayperReview <-
RentCostMerged_final %>%
group_by(neighbourhood_group_cleansed,zipcode) %>%
summarise(RvperMonth_avg=mean(number_of_reviews),RvScoreRating_med=(median(review_scores_rating))/10)%>%
ggplot(aes(x = RvperMonth_avg, y= RvScoreRating_med,colour = zipcode)) +
geom_point() +
geom_vline(xintercept = m,linetype="dashed", color = "gray")+
geom_hline(yintercept = r,linetype="dashed", color = "gray")+
#geom_point(data=ReleventZips_Rvu_sel, aes(x=RvperMonth_avg,y=RvScoreRating_med))+ #for highlighting
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1),
plot.title = element_text(hjust = 0.5, size = 14),# Center title position and size
plot.subtitle = element_text(hjust = 0.5,size = 12),# Center subtitle
plot.caption = element_text(hjust = 0, face = "italic")
) +
labs(x="Total Number Of Reviews",
y="Review Score Rating(10)",
title = "Zipcode Review Analysis",
subtitle = "Zipcodes in first quadrant are out performing",
caption = "Data source: Airbnb & Zillow"
)
ggplotly(numDayperReview)Intepretation of Plot
- They grey vertical & horizontal axis indicates the median review per month and median Review score rating.
- Zipcodes that have above-median Review score rating and reviews per month are considered better based on Review analysis.
Observation
- 6 Zipcodes perform better than median reference values
Recommended Zipcodes based on Review Dimension
#Zips recommended
aboverMedian_byReview<-subset(ReleventZips_Rvu,RvScoreRating_med>=r & RvperMonth_avg>=m)
aboverMedian_byReview## # A tibble: 6 x 4
## # Groups: neighbourhood_group_cleansed [3]
## neighbourhood_group_cleansed zipcode RvperMonth_avg RvScoreRating_med
## <fct> <fct> <dbl> <dbl>
## 1 Brooklyn 11215 30.1 9.7
## 2 Brooklyn 11201 19.4 9.7
## 3 Queens 11434 37.1 9.6
## 4 Brooklyn 11231 26.4 9.6
## 5 Staten Island 10305 25.7 9.6
## 6 Brooklyn 11217 22.5 9.64.3.4 Break even Analysis
Assumption
- Rent increases by 10% every year
- Occupancy rate for all property is calculated as 75%
An utmost important dimension in any investment is it’s break-even time. The breakeven point in this analysis is referred to the period property would take to earn its investment amount, the period when rent earned over years = investment amount
A property that can reach this point earlier starts to earn profit earlier. Hence, selecting the zipcodes that have smaller breakeven time would be a sensible investment.
#Code to calculate break even time based on current price and rent
brkEvenCalculate <- function(RentCostMerged_final){
breakeven_time <- c()
for(i in 1:nrow(RentCostMerged_final))
{
#initialize to 0 default values
TmeYr <- 0
totRevenue <- 0
aprx_Prcntg_Inc_Rnt_Prc_PrYr<-.10 #Assumption
occpncyRate<-.75 #Asumption
rentPrice <- RentCostMerged_final$totalRent[i] # rent price of property
costPrice <- RentCostMerged_final$currentPrice[i]# cost price of property
# while cost of buying the house is greater than the total revenue generated through rent
while(costPrice>totRevenue)
{
# increase year by 1
TmeYr <- TmeYr + 1
# increase rent price for next year by a fixed percentage
rentPrice <- rentPrice * (1+aprx_Prcntg_Inc_Rnt_Prc_PrYr)
# calculating revenue till the current year
totRevenue <- totRevenue + rentPrice*365*occpncyRate
}
# decrease the year by 1 as break-even point has reached before the end of this year
breakeven_year <- TmeYr - 1
# total revenue needed this year to break-even
rvnue_extra <- totRevenue - costPrice
# total time needed to break-even in this year
breakeven_year_extra_time <- rvnue_extra/(rentPrice*365*occpncyRate)
# total breakeven time is summation of previous years + time taken in the current year
breakeven <- breakeven_year + breakeven_year_extra_time
# storing breakeven time for each property
RentCostMerged_final$breakevenTime[i] <- round(breakeven,2)
}
return(RentCostMerged_final)
}
RentCostMerged_final<-brkEvenCalculate(RentCostMerged_final)Cost Rent Density Analysis
A density anlaysis of cost price and Rental income can give a clear picture of better prospects in investment.
pp_c<-ggplot(RentCostMerged_final,
aes(x=eval(parse(text='currentPrice')),
fill=eval(parse(text='neighbourhood_group_cleansed')))) +
geom_density(alpha=0.4) +
geom_vline(aes(xintercept=median(eval(parse(text='currentPrice'))))
, color="black", linetype="dashed", size=.5) +
guides(fill=guide_legend(title='Neighbourhood')) +
scale_x_continuous(limits = c(0, 5000000))+
ggtitle("Cost vs Rent analysis")+
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1),
plot.caption = element_text(hjust = 0, face = "italic")
) +
labs(x="Cost of Properties",
y="Density",
subtitle = "Cost Density plot",
title = "House Price vs Property Rent Analysis"
)
pp_r<-ggplot(RentCostMerged_final,
aes(x=eval(parse(text='totalRent')),
fill=eval(parse(text='neighbourhood_group_cleansed')))) +
geom_density(alpha=0.4) +
geom_vline(aes(xintercept=median(eval(parse(text='totalRent'))))
, color="black", linetype="dashed", size=.5) +
scale_x_continuous(limits = c(0, 1000)) +
guides(fill=guide_legend(title='Neighbourhood'))+
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1),
plot.caption = element_text(hjust = 0, face = "italic"),
) +
labs(x="Rent/Night of Properties",
y="Density",
title = "",
subtitle = "Rent Density Plot"
#caption = "Data source:Zillow"
)
grid.arrange(pp_c, pp_r)
Observation
The cost price of properties is highest in the neighbourhood Manhattan followed by Brooklyn . Also, Manhattan has the highest spread and surprising cost price of Brooklyn is very short spread. Staten Island & Queens have an overlapping cost price
The rentals/per Night of properties is closely overlapping. Manhattan has the longest spread of rental amount, whereas Brooklyn is concentrated in a shorter range. Staten Island and Queens have overlapping cost price.
Manhattan: The cost of properties at Manhattan is highest as compared to other neighbourhoods, we further notice that half of the properties at Manhattan have rentals higher than median overall rent. This indicates that breakeven time will be considerably higher.
Brooklyn: The cost of properties is higher but less than that of Manhattan, whereas the rent is quite close to that of Manhattan, a few properties of Manahattan have higher rental value than of Brooklyn. Most properties have lower than median overall rent.
Staten Island and Queens have overlapping costs and rent(Range is similar). The rent and cost both are below median values.
This analysis indicates that Brooklyn can be a better choice of investment, We can verify this through below boxplot analysis of break-even time for zipcodes
Break Even Time analysis
ggplot(RentCostMerged_final, aes(x = as.factor(zipcode), y = breakevenTime, fill = neighbourhood_group_cleansed)) +
geom_boxplot(alpha=0.5,
# custom outliers
outlier.colour="red",
outlier.fill="red",
outlier.size=1
) +
labs(x = "Zipcode", y = "Breakeven time") +
scale_y_continuous(breaks=seq(0, 40, 8)) +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
guides(fill=guide_legend(title='Neighbourhood'))+
theme(
plot.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1),
plot.caption = element_text(hjust = 0, face = "italic"),
legend.position="bottom",
) +
labs(x="Zipcode",
y='Breakeven Time (years)',
title = "Break even Time Analysis",
subtitle = "",
caption = "Data source:Airbnb & Zillow",
fill='Neighbourhood'
)
Observation Looking at the boxplots segregated by neighbourhood, zipcode we can infer that :
- Manhattan takes a lead in all neighbourhood with a considerably higher breakeven period.
- Brooklyn has a shorter break-even period than Manhattan but longer than Staten Island and Queens.
Based on the breakeven analysis the zipcodes that have shorter than median can be considered as good investment locations.
Recommended Zipcodes based on breakeven analysis:
#all relevent zips as per break even analysis
ReleventZips_BEA<-
RentCostMerged_final%>%
group_by(neighbourhood_group_cleansed,zipcode) %>%
summarise(breakEven_med=median(breakevenTime)) %>%
arrange(breakEven_med)
#zips above median
aboveMedian_byBEA<-subset(ReleventZips_BEA,breakEven_med<=median(ReleventZips_BEA$breakEven_med))
aboveMedian_byBEA## # A tibble: 9 x 3
## # Groups: neighbourhood_group_cleansed [4]
## neighbourhood_group_cleansed zipcode breakEven_med
## <fct> <fct> <dbl>
## 1 Queens 11434 7.32
## 2 Staten Island 10305 8.92
## 3 Manhattan 10022 9.3
## 4 Manhattan 10036 9.43
## 5 Brooklyn 11231 10.2
## 6 Brooklyn 11215 10.4
## 7 Manhattan 10025 10.7
## 8 Brooklyn 11201 10.7
## 9 Brooklyn 11217 11.65.0 Conclusion
Below table indicates Recommended zipcodes based on 4 important dimensions.
Median value is considered as reference in each category
Interpretation of above table:
- The zip code that are present in all 4 columns are the ones that are better performing zips in all 4 dimensions.
- Ranking as per individual dimension analysis is maintained.For instance, 11215 performed in the preference category as compared to 11217.
Conclusion
- There are 3 zipcode that are present in all 4 dimensions, namely: 11215, 11217,11231 ,all these are from Brooklyn. These 3 are recommended Zipcodes for Investment
- There is 1 zipcode that is present in 3 dimensions(Demand, Review, Breakeven), namely:11201, from Brooklyn.
- Many zips are present in 2 dimensions as it can be noticed from the table.
Future Steps
- Speaking to the investor to understand the type of investment and dimension of interest and help choose the zip codes further.
- With stated Assumptions and Data, the analysis is made on some assumed Dimensions of interest.
- Deeper understanding of each Attribute and significance will facilitate in making better decision models.
- Text and Sentimental analytics on Review column can help understand if the review is positive or Negative. This can bring deeper insight into criteria that attract the customer population.
- Introducing Weather data, Holiday Season & Special Day Celebrations Data can improve the scalability of the analysis.