Problem Statement:

To analyze the given data to identify the zipcodes in New York that are worth investing for most profit on short term rentals.

Data Available:

For this purpose, publicly available data from Zillow and AirBnB were used.

  1. Cost data: Zillow provides us an estimate of value for two-bedroom properties.
  2. Revenue data: AirBnB is the medium through which the investor plans to lease out their investment property.

Assumptions:

  1. The investor will pay for the property in cash (i.e. no mortgage/interest rate will need to be accounted for).
  2. The time value of money discount rate is 0% (i.e. $1 today is worth the same 100 years from now).
  3. All properties and all square feet within each locale can be assumed to be homogeneous (i.e. a 1000 square foot property in a locale such as Bronx or Manhattan generates twice the revenue and costs twice as much as anyother 500 square foot property within that same locale.)
  4. The room types ‘Entire house’ and ‘Private room’ are considered the same. The price given for the stay at the property is considered the total price irrespective of the room type.
  5. Availablity for next 365 days is a considered as the metric for occupancy. Lesser than availablity , higher the occupancy.
  6. Occupancy rate is calculated by subtracting the total available days by total number of days in year and dividing it by 365.
  7. Revenue is calculated o basis of price per night. It is assumed the security deposit is returned to the customer once they checkout and the cleaning fees is spent in cleaning the property.
  8. Number of reviews is considered a positvie trait for any property. Higher count of reviews shows more engagement.
  9. Availability within 30, 60 ,90 and 365 days are considered highly similar(as shown later). Yearly availability is taken into analysis because tourism is seasonal in nature.

Key metrics:

For this purpose of getting the top zipcodes there are some key metrics that I’ll base my analysis on. Some of these metrics are assumptions whereas the others are researched based on information on property evaluation.

  1. Reviews: Number of reviews on a property depict a lot about the property. More number of reviews mean the property has been booked more often as compared to properties with fewer number of reviews. A zipcode that comprises of properties with more reviews has high engagemnent with users and more probable to be booked in future.

  2. Occupancy Rate: It is the percentage of occupancy of the property in a given month/year. Greather the availability , lesser is the occupancy. More occupancy will A property that is occupied 365 days in a year has a occupancy rate of 100% whereas a property.

  3. Annual Return: It is the revenue generated from the property on a yearly basis. For the purpose of this analysis, the annual return is calculated by multiplying the price per night, yearly occupancy rate and 365 days. (price/night* yearly_occupancy * 365)

  4. Rent Ratio: It’s the annual rent collected from the property divided by the price of the property. The higher the rent ratio the better. It means either that the property is bought at a cheap rate or the rent is higher. In either of these cases, the ivestor is at gain.

  5. Break Even Time: Break-even time represents the amount of time it takes for an investment to make back its original cost. It is caluclated here by dividing the original cost of the proerty by annual rent generated by the property. But since the rent for the property will not be same every year, an increase of 10% in the rent genrated yearly is considered. So lesser is the break even time, the better it is for the investor since after the break event time, most of the revenue will be a profit for the investor and properties with high break even time will take longer to even out the expenes.

Process Followed:

  1. Package Loading: Getting the required packages.
  2. Data Loading: Cost and Revenue file were loaded. They contain 262 and 106 rows respectively.
  3. Data Preparation: Cost and Revenue datasets are handled separately in an attempt to enrich the data quality for exploratory data analysis.
    1. Missing value and Duplicate values check: Data is checked for missing values and duplicate values.Columns with missing value percentage > 50% is dropped.
    2. Data Cleaning: Dropping columns not required for analysis. Dropping the dollar sign from price and changing the format to numeric type.
  4. Merging the datasets: Merging with the revenue file to get property cost of 2 bedroom apartments on the zipcodes.
  5. Calculating Key Metrics: Creating key metrics and adding them to the dataframe as columns.
  6. Exploratory Data Analysis: Visualizaing the data by plots and graphs to study and understand the key metrices and strengthen the analysis. Revenue and Cost data were joined based on zipcodes and final data set contains 25 zipcodes to choose from,expanding over 4 major neighbourhoods - Manhattan, Queens, Brooklyn and Staten Island.

Key Findings:

  1. Most of zipcodes in Manhattan and Brooklyn have higher property cost. Since these zipcodes are also popular in terms of tourism they make some excellent choice for investment.

  2. Price and Availablity are not inveresely proportional. Customers are not hestitant to pay higher price ahead of time. The company can capitalize this behavior into their pricing scheme.

  3. Staten Island and Queens Neighbourhood have limited choices but it also gets fewer guests. In case the company wants to diversify, they should pick the top zips from different neighbourhoods to minimize risk.

Top Zipcodes:

  1. 11434
  2. 10003
  3. 10013
  4. 11201
  5. 11217 and 10014

Conclusion:

  1. Zipcode 11434 comes at first, It fits 3 out of 5 metrics.
  2. Zipcode 10003 gets the second place , it also fits 3 out of 5 metrics.
  3. Zipcode 10013 is also very profound, it is on the top on 2 metrics. With a higher rent ratio and annual return it can prove to be very profitable.
  4. 11201 comes in 2nd and 3rd position for Occupancy percentage and break even time.
  5. 11217 and 10014 are also good choices since both of these perform good in 2 metrics.

Loading Data:

Loading the Cost(zillow) and Revenue(airbnb) datasets

Data Preparation

Cost Data

RegionID RegionName City State Metro CountyName SizeRank 1996-04 1996-05 1996-06 1996-07 1996-08 1996-09 1996-10 1996-11 1996-12 1997-01 1997-02 1997-03 1997-04 1997-05 1997-06 1997-07 1997-08 1997-09 1997-10 1997-11 1997-12 1998-01 1998-02 1998-03 1998-04 1998-05 1998-06 1998-07 1998-08 1998-09 1998-10 1998-11 1998-12 1999-01 1999-02 1999-03 1999-04 1999-05 1999-06 1999-07 1999-08 1999-09 1999-10 1999-11 1999-12 2000-01 2000-02 2000-03 2000-04 2000-05 2000-06 2000-07 2000-08 2000-09 2000-10 2000-11 2000-12 2001-01 2001-02 2001-03 2001-04 2001-05 2001-06 2001-07 2001-08 2001-09 2001-10 2001-11 2001-12 2002-01 2002-02 2002-03 2002-04 2002-05 2002-06 2002-07 2002-08 2002-09 2002-10 2002-11 2002-12 2003-01 2003-02 2003-03 2003-04 2003-05 2003-06 2003-07 2003-08 2003-09 2003-10 2003-11 2003-12 2004-01 2004-02 2004-03 2004-04 2004-05 2004-06 2004-07 2004-08 2004-09 2004-10 2004-11 2004-12 2005-01 2005-02 2005-03 2005-04 2005-05 2005-06 2005-07 2005-08 2005-09 2005-10 2005-11 2005-12 2006-01 2006-02 2006-03 2006-04 2006-05 2006-06 2006-07 2006-08 2006-09 2006-10 2006-11 2006-12 2007-01 2007-02 2007-03 2007-04 2007-05 2007-06 2007-07 2007-08 2007-09 2007-10 2007-11 2007-12 2008-01 2008-02 2008-03 2008-04 2008-05 2008-06 2008-07 2008-08 2008-09 2008-10 2008-11 2008-12 2009-01 2009-02 2009-03 2009-04 2009-05 2009-06 2009-07 2009-08 2009-09 2009-10 2009-11 2009-12 2010-01 2010-02 2010-03 2010-04 2010-05 2010-06 2010-07 2010-08 2010-09 2010-10 2010-11 2010-12 2011-01 2011-02 2011-03 2011-04 2011-05 2011-06 2011-07 2011-08 2011-09 2011-10 2011-11 2011-12 2012-01 2012-02 2012-03 2012-04 2012-05 2012-06 2012-07 2012-08 2012-09 2012-10 2012-11 2012-12 2013-01 2013-02 2013-03 2013-04 2013-05 2013-06 2013-07 2013-08 2013-09 2013-10 2013-11 2013-12 2014-01 2014-02 2014-03 2014-04 2014-05 2014-06 2014-07 2014-08 2014-09 2014-10 2014-11 2014-12 2015-01 2015-02 2015-03 2015-04 2015-05 2015-06 2015-07 2015-08 2015-09 2015-10 2015-11 2015-12 2016-01 2016-02 2016-03 2016-04 2016-05 2016-06 2016-07 2016-08 2016-09 2016-10 2016-11 2016-12 2017-01 2017-02 2017-03 2017-04 2017-05 2017-06
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

Restructuring the data.

We have data for every months from 2008 to 2017. It means there are (12*10) columns of price data for the past 10 years, which is cumbersome to process and analyse. Median is chosen as opposed to Mean to guard against large outlier values which may lead to bias in analysis.

Process: 1) Calculate median price of each individual zipcodes across the years from 2008 to 2017. 2) Asign the median price calculated to the respective zipcodes.

## # A tibble: 2,850 x 3
##    RegionName year  median_price
##    <chr>      <chr>        <dbl>
##  1 10003      2008       1532950
##  2 10003      2008       1532950
##  3 10003      2008       1532950
##  4 10003      2008       1532950
##  5 10003      2008       1532950
##  6 10003      2008       1532950
##  7 10003      2008       1532950
##  8 10003      2008       1532950
##  9 10003      2008       1532950
## 10 10003      2008       1532950
## # … with 2,840 more rows

Median price over the past 10 years (2008-2017)

We now have median price for every zipcode for every year from 2008 to 2017 i.e for 25 zipcodes and 10 years.Plotting the distribuition of median price over the past 10 years (2008-2017) to see the trend in the real estate market.

As seen from the plot above, the prices of houses are fluctuating for some zipcodes. While for other zipcodes it has been consistent moslty. So it’s better not consider these years for the analysis and start with 2014 to get less variability in prices. Dropped the years from 2008 to 2013.

Predicting median price for 2019

The dataset has latest prices for year 2017, but for the analysis in 2019 I’ll try to predict the median for the year 2019. The approach is to calculate the growth for every consecutive year and then take an average of it. And this average growth per year is used to calculate the prices for year 2019.

The columns year1,year2 and year3 shows the relative increase in the prices of house from the previous year. The avg_growth column is the average of year1,year2 and year3. 2018 and 2019 shows the predicted prices for year 2018 and 2019.

## # A tibble: 6 x 3
##   RegionName year     price
##   <fct>      <chr>    <dbl>
## 1 10003      2019  2208878.
## 2 10011      2019  2601297.
## 3 10013      2019  3320756.
## 4 10014      2019  2776339.
## 5 10021      2019  1905416.
## 6 10022      2019  1988830.

Removing the columns we don’t need and renaming the column 2019 to price, which is the predicted price for year 2019. This is the final cleansed cost data that is used further in this analysis.

Median price of properties in 2019

Plotting median price(predicted) of properties in 2019 by zipcodes.

Zipcodes 10013,10014,10011,10003 and 10023 are the top 5 costliest in terms of property unlike 10303,10304,10306,10312 and 10314 which are the cheapest zipcodes to buy properties in New York City.

Data Preparation

Revenue data

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. NA NA -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 FALSE 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’] TRUE TRUE Brooklyn , NY, United States Brooklyn Kensington Brooklyn Brooklyn NY 11218 New York Brooklyn , NY US United States 40.64749 -73.97237 FALSE 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 TRUE 30 60 90 365 2019-07-09 9 2 2015-12-04 2018-10-19 98 10 10 10 10 10 10 FALSE NA NA FALSE FALSE moderate FALSE FALSE 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
  • 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 subway connections in the very desirable Midtown location a few minutes walk to Times Square, the Theater District, Bryant Park and Herald Square. - The Midtown Castle is a uniquely charming Dutch Colonial survivor from the 1890s. - This is
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. NA 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 FALSE 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’] TRUE TRUE New York, NY, United States Manhattan Midtown Manhattan New York NY 10018 New York New York, NY US United States 40.75362 -73.98377 FALSE 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 NA $350.00 $100.00 2 $0.00 1 1125 1 1 1125 1125 1 1125 4 days ago TRUE 25 55 80 355 2019-07-09 45 11 2009-11-21 2019-05-21 95 10 9 10 10 10 9 FALSE NA NA FALSE FALSE strict_14_with_grace_period TRUE TRUE 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 ! NA 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 NA NA NA NA NA 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 FALSE 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’] TRUE TRUE New York, NY, United States Harlem Harlem Manhattan New York NY 10027 New York New York, NY US United States 40.80902 -73.94190 TRUE 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 NA NA $200.00 $75.00 2 $20.00 3 7 3 3 7 7 3 7 34 months ago TRUE 30 60 90 365 2019-07-08 0 0 NA NA NA NA NA NA NA NA NA FALSE NA NA FALSE FALSE strict_14_with_grace_period TRUE TRUE 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. NA 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 FALSE 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’] TRUE TRUE Brooklyn, NY, United States Brooklyn Clinton Hill Brooklyn Brooklyn NY 11238 New York Brooklyn, NY US United States 40.68514 -73.95976 TRUE 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 NA 1 $0.00 1 730 1 1 730 730 1 730 today TRUE 0 0 3 194 2019-07-09 270 69 2014-09-30 2019-07-05 90 10 9 10 10 10 9 FALSE NA NA FALSE FALSE moderate FALSE FALSE 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 NA Loft apartment with high ceiling and wood flooring located 10 minutes away from Central Park in Harlem - 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 - 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 NA NA NA NA NA 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 FALSE 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’] TRUE TRUE 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 TRUE 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 TRUE 0 0 0 0 2019-07-08 9 4 2012-03-20 2018-11-19 93 10 9 10 10 9 10 FALSE NA NA FALSE FALSE strict_14_with_grace_period TRUE TRUE 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 FALSE 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’] TRUE FALSE 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 FALSE 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 NA NA $300.00 $125.00 2 $100.00 3 21 3 3 21 21 3 21 3 weeks ago TRUE 23 48 48 129 2019-07-08 74 9 2009-04-20 2019-06-22 89 10 9 10 10 9 9 FALSE NA NA FALSE FALSE strict_14_with_grace_period TRUE TRUE 1 1 0 0 0.59

Revenue is a large dataset with 48000 rows and 106 columns. Before checking for missing values Removing the columns in the airbnb dataset that has information related to the hosts, url and other data points not related to our analysis.

Finalizing the Revenue data

## [1] 48895    23

We now have 23 columns in the revenue data.

Merging the datasets

Merging the cost(zillow) datset with revenue(airbnb) dataset on zipcodes and checking for duplicates and missing values in our final dataset.

Creating a copy for the final data. In case of any discrepencies later during the analysis, there’s no need to read and load the data again. Final data can be loaded and continued.

Creating a function to clean the price columns. This fucntion removes any spaces and/or $ signs then converts type to numeric from character.

Final clean revenue data

This is the final cleansed dataset that will be used henceforth. Here’s a summary of the dataset.

##     zipcode            id            last_scraped       
##  Min.   :10003   Min.   :   16458   Min.   :2019-07-08  
##  1st Qu.:10014   1st Qu.: 7664343   1st Qu.:2019-07-08  
##  Median :10028   Median :17586014   Median :2019-07-08  
##  Mean   :10426   Mean   :17837441   Mean   :2019-07-08  
##  3rd Qu.:11215   3rd Qu.:28501387   3rd Qu.:2019-07-09  
##  Max.   :11434   Max.   :36477307   Max.   :2019-07-09  
##                                                         
##  neighbourhood_group_cleansed     city              latitude    
##  Length:1565                  Length:1565        Min.   :40.52  
##  Class :character             Class :character   1st Qu.:40.68  
##  Mode  :character             Mode  :character   Median :40.73  
##                                                  Mean   :40.73  
##                                                  3rd Qu.:40.76  
##                                                  Max.   :40.81  
##                                                                 
##    longitude      property_type       room_type          accommodates   
##  Min.   :-74.21   Length:1565        Length:1565        Min.   : 1.000  
##  1st Qu.:-74.00   Class :character   Class :character   1st Qu.: 4.000  
##  Median :-73.99   Mode  :character   Mode  :character   Median : 4.000  
##  Mean   :-73.98                                         Mean   : 4.458  
##  3rd Qu.:-73.97                                         3rd Qu.: 5.000  
##  Max.   :-73.76                                         Max.   :16.000  
##                                                                         
##    bathrooms        bedrooms      beds        square_feet          cost       
##  Min.   :0.000   Min.   :2   Min.   :0.000   Min.   :   0.0   Min.   :  50.0  
##  1st Qu.:1.000   1st Qu.:2   1st Qu.:2.000   1st Qu.: 650.0   1st Qu.: 165.0  
##  Median :1.000   Median :2   Median :2.000   Median :1000.0   Median : 228.0  
##  Mean   :1.299   Mean   :2   Mean   :2.381   Mean   : 902.3   Mean   : 284.6  
##  3rd Qu.:2.000   3rd Qu.:2   3rd Qu.:3.000   3rd Qu.:1125.0   3rd Qu.: 320.0  
##  Max.   :3.500   Max.   :2   Max.   :6.000   Max.   :1600.0   Max.   :4000.0  
##  NA's   :3                                   NA's   :1538                     
##  security_deposit   cleaning_fee       availability_30  availability_60
##  Length:1565        Length:1565        Min.   : 0.000   Min.   : 0.00  
##  Class :character   Class :character   1st Qu.: 0.000   1st Qu.: 0.00  
##  Mode  :character   Mode  :character   Median : 0.000   Median : 7.00  
##                                        Mean   : 5.894   Mean   :15.87  
##                                        3rd Qu.: 8.000   3rd Qu.:27.00  
##                                        Max.   :30.000   Max.   :60.00  
##                                                                        
##  availability_90 availability_365 number_of_reviews review_scores_rating
##  Min.   : 0.00   Min.   :  0.0    Min.   :  0.00    Min.   : 20.00      
##  1st Qu.: 0.00   1st Qu.:  0.0    1st Qu.:  1.00    1st Qu.: 92.00      
##  Median :12.00   Median : 59.0    Median :  4.00    Median : 96.00      
##  Mean   :25.79   Mean   :120.7    Mean   : 19.78    Mean   : 94.15      
##  3rd Qu.:49.00   3rd Qu.:247.0    3rd Qu.: 17.00    3rd Qu.:100.00      
##  Max.   :90.00   Max.   :365.0    Max.   :403.00    Max.   :100.00      
##                                                     NA's   :387         
##      year              revenue       
##  Length:1565        Min.   : 370485  
##  Class :character   1st Qu.:1487596  
##  Mode  :character   Median :1770053  
##                     Mean   :1896679  
##                     3rd Qu.:2208878  
##                     Max.   :3320756  
##
zipcode id last_scraped neighbourhood_group_cleansed city latitude longitude property_type room_type accommodates bathrooms bedrooms beds square_feet cost security_deposit cleaning_fee availability_30 availability_60 availability_90 availability_365 number_of_reviews review_scores_rating year revenue
10003 9905142 2019-07-08 Manhattan New York 40.73036 -73.98673 Apartment Entire home/apt 2 1.5 2 2 NA 250 $200.00 $95.00 0 13 13 13 20 93 2019 2208878
10003 14759888 2019-07-08 Manhattan New York 40.73029 -73.98725 House Private room 2 1.0 2 2 NA 95 NA $25.00 0 0 0 0 9 91 2019 2208878
10003 30014439 2019-07-08 Manhattan New York 40.73015 -73.98447 Apartment Entire home/apt 4 1.0 2 3 NA 350 $500.00 $80.00 5 5 5 5 3 80 2019 2208878
10003 34642119 2019-07-08 Manhattan New York 40.72877 -73.98848 Apartment Entire home/apt 5 1.0 2 2 NA 200 $150.00 $100.00 2 9 23 69 13 92 2019 2208878
10003 20028846 2019-07-08 Manhattan New York 40.73726 -73.99280 Apartment Entire home/apt 5 1.0 2 2 NA 230 $0.00 $60.00 5 5 5 5 5 100 2019 2208878
10003 21783251 2019-07-08 Manhattan New York 40.72909 -73.99125 Apartment Entire home/apt 4 1.0 2 2 NA 200 $175.00 $50.00 3 7 7 194 81 95 2019 2208878

Exploratory Data Analysis

Plotting the distribution of missing variables in our final data.

Most of the property in our analysis will deal with Apartment. It does makes sense though looking at properties in New York City, the apartment count would be a lot larger than other house types.

Checking the number of properties by neighbourhood

Checking the price per night of properties by neighbourhood.

Checking the number of properties by zipcodes.

Some zipcodes have just 1, 2 or 3 properties, which is a small number for analysis. So these zipcodes would not be conisdered for the analysis. Dropping the zipcodes with less than 10 properties

Number of properties by type

Plotting the number of properties by their type. We see that mostly the property in our datset are Apartments.

Price distribution of properties

Checking the price distribution of properties according to property types

It is evident that apartments are costlier than any other type of property and apartments in zipcodes 11217 and 10003 are the cosltiest for a night. Checking the variation in price per night of the properties across the zipcodes along with zipcodes.We see there are some properties in Manhattan which are way above the avergae prices.

It is noticeable that there are a lot of outliers in the price per night.Specially the zipcodes 10013 and 10003 have the costilest staying prices.

Visualizing the price per night of the properties by zipcodes

Computing and analysing key metrics

1) Number of reviews

This column that has the count of reviews on a property is already present in our dataset.

Checking the average number of reviews of the properties by zipcode.

The top zipcodes based on number of reviews are 11434,11215,11231,10305 and 10003.

2) Availability and Occupancy Percentage

Availabiity

Availabiity of a property should be balanced. Less availability means the property is in high demand and is good for the property owner but higher availability means the property is not being booked and is a negative sign. Higher availability or lower occupancy can be attributed to many reasons, such as 1) High prices 2) Lesser/No tourist attractions 3) High crime rates

We have 30, 60, 90 and 365 days availability. All of these can be used for analysis. It’s important to check how these variables corelate to each other.

For the purpose of this study, yearly avaialbility (availability_365) is a better choice as it shows the bigger picture and being highly correlated with other variables, it will complement our analysis.

Occupancy Percentage

Occupancy is the opposite of availability. Higher the occupancy the better it is for the property owners and the investors. Higher occupancy in properties is a good sign and properties in these areas will bring in more profits.

Calculating the occupancy rate explanation: Yearly occupancy rate is calculated by subtracting the number of available days by 365 and dividing it by 365. e.g. A property available for 100 days, means it is occupied for 265 days. so the occupancy percentage is 265*100/365= 72.6%.

Properites with 0% occupancy rates have been assigned extremely small values to avoid interference in calculating annual return.

Yearly occupancy percentage by price per night

This establishes the claim that lower prices doesn’t prove higher occupancy. People are ready to pay higher prices for poperties. But people prefer not to go too high with the prices as zipcodes with not too pricey properties have the highest occupancy.

The highly occupied zipcodes are 11217, 11201, 10021, 11231, 10014 and 10025.

Yearly Occupancy percentage of properties by zipcodes.

Zipcodes 11217,11201,11434, 10021 and 11231 have the highest yearly occupancy percentages.

3) Rent Ratio:

Rent ratio is the measure of price of the property divided by the gross annual rent collected. Higher the rent ratio the better it is. A higher rent ratio suggests 2 things. 1) Either the property was bought at a cheaper price. 2) Or, the rent of the property is higher In either of these cases the investor is at profit. It is an important factor to be kept in mind while buying a property.

Checking the rent ratio by zipcodes.

Rent ratio is the highest for properties in zipcodes 10013,10014,10021,10128,10003.

4) Annual Return or Return on Investment

Annual Return is the revenue generated from the property on a yearly basis. For the purpose of this analysis, the annual return is calculated by multiplying the price per night, yearly occupancy rate and 365 days. (price/night x yearly_occupancy x 365). Higher the annual return, higher is the profit.

Annual return by zipcodes

The zipcodes 10013,10011,10014,10003 and 10036 performs really good in this aspect.

5) Break even time

Break-even time represents the amount of time it takes for an investment to make back its original cost. It is caluclated here by dividing the original cost of the proerty by annual rent generated by the property. But since the rent for the property will not be same every year, an increase of 10% in the rent genrated yearly is considered.

Explanation: A property that was bought for 100,000 and generates an annual return(in 2019) of $20,000. The annual return will increase by 10% every year i.e in 2020 it will be 22,000 and 24,200 in 2021. Basically we multiply the price of current year by 1.1 to calculate the annual return next year.

The break even time for this property will be the year when total annual return collected will be equal to the original cost of property. i.e (100000/20000+22000+24200+26620+29282) ~ approx 4.5 years. So lesser is the break even time, the better it is for the investor. After the break event time, most of the revenue will be a profit for the investor.

Visualizing the average break even times by zipcodes

Final data set with added columns

zipcode id last_scraped neighbourhood_group_cleansed city latitude longitude property_type room_type accommodates bathrooms bedrooms beds square_feet cost security_deposit cleaning_fee availability_30 availability_60 availability_90 availability_365 number_of_reviews review_scores_rating year revenue occupancy_rt_30 occupancy_rt_60 occupancy_rt_90 occupancy_rt_year rent_ratio annual_return break_even_time
10003 9905142 2019-07-08 Manhattan New York 40.73036 -73.98673 Apartment Entire home/apt 2 1.5 2 2 NA 250 $200.00 $95.00 0 13 13 13 20 93 2019 2208878 100.00000 78.33333 85.55556 96.43836 24.54309 88000 34
10003 14759888 2019-07-08 Manhattan New York 40.73029 -73.98725 House Private room 2 1.0 2 2 NA 95 NA $25.00 0 0 0 0 9 91 2019 2208878 100.00000 100.00000 100.00000 100.00000 64.58708 34675 44
10003 30014439 2019-07-08 Manhattan New York 40.73015 -73.98447 Apartment Entire home/apt 4 1.0 2 3 NA 350 $500.00 $80.00 5 5 5 5 3 80 2019 2208878 83.33333 91.66667 94.44444 98.63014 17.53078 126000 31
10003 34642119 2019-07-08 Manhattan New York 40.72877 -73.98848 Apartment Entire home/apt 5 1.0 2 2 NA 200 $150.00 $100.00 2 9 23 69 13 92 2019 2208878 93.33333 85.00000 74.44444 81.09589 30.67886 59200 38
10003 20028846 2019-07-08 Manhattan New York 40.73726 -73.99280 Apartment Entire home/apt 5 1.0 2 2 NA 230 $0.00 $60.00 5 5 5 5 5 100 2019 2208878 83.33333 91.66667 94.44444 98.63014 26.67727 82800 35
10003 21783251 2019-07-08 Manhattan New York 40.72909 -73.99125 Apartment Entire home/apt 4 1.0 2 2 NA 200 $175.00 $50.00 3 7 7 194 81 95 2019 2208878 90.00000 88.33333 92.22222 46.84932 30.67886 34200 44

Zipcodes 11434,10305,11201,11217 and 10025 have the lowest break even times.

Conclusion and Future Steps:

Evaluation :

The zipcodes were analysed on the key metrics as described above and the top 5 zipcodes suggested by each of these metrics are as follows:

Reviews Occupancy Rent Ratio Annual Return Break even time
11434 11217 10013 10013 11434
11215 11201 10014 10011 10305
11231 11434 10021 10014 11201
10305 10021 10128 10003 11217
10003 11231 10003 10036 10025

We’ll take the zipcodes that managed to make it to 2 or more metrics.

Top Zipcodes
11434
10003
10013
11201
11217 & 10014

Preference has been given to those zipcodes which appear in 3 metrics. Rent ratio and Annual return has been preffered over occupancy percentage. So above mentioned are the possible suggested zipcodes for investments. However, it is necessary to discuss with the firm as to what types of risk they are willing to take.

Conclusion:

  1. Zipcode 11434 comes at first, It fits 3 out of 5 metrics.
  2. Zipcode 10003 gets the second place , it also fits 3 out of 5 metrics.
  3. Zipcode 10013 is also very profound, it is on the top on 2 metrics. With a higher rent ratio and annual return it can prove to be very profitable.
  4. 11201 comes in 2nd and 3rd position for Occupancy percentage and break even time.
  5. 11217 and 10014 are also good choices since both of these perform good in 2 metrics.

Future Steps:

  1. Time series forecastiing techniques like ARIMA can be used for better and accurate prediction of cost of property at a future date.
  2. Price per night are taken constant throughout the year, but ususally, the prices aren’t stable and so they fluctuate depending on the time of the year.
  3. Text Analytics / Sentiment Analytics would open insights about other metrics that drive customer to book an airbnb property such as neighborhood descriptions, property descriptions and reviews.