Data set can be found here: https://docs.google.com/spreadsheets/d/1SEivaWwc59nU

Question

Data set can be found here:
https://docs.google.com/spreadsheets/d/1SEivaWwc59nUBJSwJt-wHYM7LLuxqPYz6CvPyYln4Rk/edit?usp=sharing

Yoo et al. (2014) recently studied the impacts of a variety of factors influencing house prices in Prescott, Arizona. Prescott metropolitan area in Central Arizona includes five recreational lakes – Granite Basin Lake, Watson Lake, Willow Creek Reservoir, Lynx Lake and Upper Goldwater Lake. The five lakes in the study area provide a range of benefits – recreational opportunity, scenic beauty, clear water, etc - to the residents of Prescott, and hence easier access to those lakes is expected to increase nearby residential property prices. Higher level of sedimentation in the lake or rivers leads to physical disruption of water quality and creates high levels of turbidity by limiting penetration of sunlight into the water column. Therefore, higher level of sedimentation is expected to decrease nearby residential property values. Using a regression model(s), you will investigate a variety of environmental, structural, and socio-economic factors that affect residential property values using this dataset (Prescott_Parcel.xlsx).
Description of variables included in the dataset is shown below:
Variable
Description
Price
Land_fcv
Age
Time
Pop_sqml
Gr_fl_ar
Patio_Floor
sedi_per_lake
Imp_fcv
Residential property sales in dollar (Year: 2003)
Land full cash value in dollar (reflection of the market value of your property and consists of land and improvements)
The age of property
Traveling time to the nearest lake in minute
Number of population/mile2 in 2000 (measured on a Census tract level)
The area of ground floor (sq)
The ratio of patio area to total area (Total area=ground floor area+patio area)
Tons of sediment loads/lake acre in the nearest lake from each residential property
Improved full cash value

1) What are the research questions you want to investigate using this dataset?
2) Based on a multiple linear regressionmodel, which variables are significant, and which variables are not? Did the signs of each variable come out as expected?
3) What does the results from the linear regression model show about the economic value of access to recreational lakes?
4) Does water quality have a significant impact on house prices?
5) Are structural variables statistically significant and their signs come out as expected?
6) How would you explain the impact of population density on housing price?
7) Based on the results, what would you suggest property developers, house buyers, or local
government decision makers should do to improve the environmental and economic
quality (welfare) oflocal community?

Explanation / Answer

1) The research question we will investigate is how are the other variables affecting the price variable

2)We run a regression on the data with y variable as price and x variables are all the other variables

Significant Variables:

gr_fl_ar

imp_fcv

Patio_Floor

Insignificant Variable:

Yes the coefficients of all variables have the expected signs

3) The coefficient of time is -442.092 which implies that for every 1 unit increase in time taken to travel to the nearest lake we have 442.092 units decreased from the price

Hence the economic value of access to recreactional lakes is positive because as time to travel decreases the price will increase

4)Tons of sediment loads/lake acre in the nearest lake from each residential property is having a positive effect on price as the coefficient is 0.5826

ie as there will be 1 unit increase in Tons of sediment loads/lake acre in the nearest lake from each residential property there will be 0.5826 unit increase in price

5)

Yes there is a significant impact

We see that

931143.7

This means for every 1 unit change in Patio floor ratio there will be 931143.7increase in price

6)

We see that

-5.05444

From th coefficient we can say that for every 1 unit rise in popolation density we have -5.05 effect on price

7)

We would reccommed the government to give some kind of benefit to young people to buy the houses in the area as age is inversely related to prices

SUMMARY OUTPUT Regression Statistics Multiple R 0.843685 R Square 0.711804 Adjusted R Square 0.710645 Standard Error 41982.31 Observations 1999 ANOVA df SS MS F Significance F Regression 8 8.66E+12 1.08E+12 614.3775 0 Residual 1990 3.51E+12 1.76E+09 Total 1998 1.22E+13 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -128319 18492.74 -6.93888 5.33E-12 -164586 -92051.7 -164586 -92051.7 land_fcv 1.500486 0.062291 24.08845 1E-112 1.378325 1.622648 1.378325 1.622648 age -154.974 65.04088 -2.38272 0.017279 -282.529 -27.4187 -282.529 -27.4187 time -442.092 105.0791 -4.20723 2.7E-05 -648.169 -236.015 -648.169 -236.015 pop_sqml -5.05444 1.436696 -3.5181 0.000444 -7.87203 -2.23686 -7.87203 -2.23686 gr_fl_ar 97.58334 3.713844 26.27556 7E-131 90.29991 104.8668 90.29991 104.8668 Patio_Floor 931143.7 134094.4 6.94394 5.14E-12 668163.5 1194124 668163.5 1194124 sedi_per_lake 0.582667 1.079522 0.539746 0.589433 -1.53445 2.69978 -1.53445 2.69978 imp_fcv 0.276398 0.023326 11.84947 2.39E-31 0.230653 0.322144 0.230653 0.322144

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