Project Deliverable #2

Introduction

Imagine you lying on the beach with your children playing around and your chicken is roasted in the kitchen behind you. What a beautiful life. Can you refuse a house in California? I guess you can’t. Neither can I! But before buying/investing a house, we may need to look at the trend of the house selling price in recent years and the difference among various counties in CA.

Data Set

The data is coming from “Zillo”. One of the dataset includes housing prices data in the US. (Data Source)

I will choose those housing data specifically in different counties of California in the recent five or may be more years.

variabls included:

• state code
• county
• time
• median sold price
• …

dat <- read.csv("C:/Users/ruby1/Documents/WFU_R/WFUR/project2/county.csv")
head(dat,2)[c(1:3,258:262)]

##   RegionID  RegionName State X2017.02 X2017.03 X2017.04 X2017.05 X2017.06
## 1     3101 Los Angeles    CA   556100   560000   563300   566200   568500
## 2      139        Cook    IL   212700   213700   214200   214800   215300

Methodology

• Basic Statistic summary
  • distribution.
  • mean, median, std deviation, etc.
• Regression modeling
  • potential macro-economic variables influencing price: GDP, CPI, Mortgage rate, etc.

Visualization

Different plots may include, but not limited to:

• histogram
• barchart
• boxplot
• linechart

Give-away

• Understand the price trend of CA real estate in recent years.
• Understand the different prices in different ereas of CA.
• Get to know the prediction of the price trend in the near future influenced by macro-economic variables.