Hypothesis for this paper is that new urbanist features has a positive impact on property values. A detailed regression model is discussed in this research paper.
Summary
Some of the characteristics of new urbanist communities are small lots containing houses with front porches set close to the street; neighborhoods with plenty of public space; controlled landscape; and pedestrian friendly streets. It stands out from competing conventional housing stock. New urbanist communities have gained popularity either due to the changing demographics or change in taste. Despite this growing popularity, not much research is done to find out the market acceptance of such communities.
For the purpose of this research, a local housing community with the above mentioned characters is identified. Hedonics analysis method is used with sales price as the dependent variable and “new urban” (proxy for new urbanist characters) as one of the independent variables along with other independent control variables.
Results
The stepwise-stepwise regression model has adjusted R square value increased with addition of every variable and regression model explained 88% variation in sale price (adjusted R square = 0.880). The model selects other six independent variables which are also statistically significant. The coefficients for these variables show strong relationship to the sales price as expected at a significance level (min. 0.000**, and max 0.003**), indicating very less chances of accepting null hypothesis. The variable of interest ‘new urban’ show strong positive relation to the sales price ($47,000) with t static (8.7) at significance level (0.000**).
The residual plots for standardized residual to adjusted predicted value do not show any violation of linearity and statistical independence assumption. Little flared pattern is observed mainly due to some higher sales prices in newer homes. It can be attributed to the homes built when the real estate markets were unrealistically bullish and certain custom built homes. Since number of such transactions is low, adjusted weight method technique was not applied. Also the outlier identified show relatively low standard of error i.e. less than four.
To make sure the price differential is not due the age of these communities (newly built) and desired school district, three dummy variables to describe age and one for school district are manually entered. Of those, the model only showed one of the variables i.e. homes less than eight years in age, has statistically positive relation to the sales price ($18821) with t statistics of (2.82) at significance level (0.005**).
Conclusion
The results confirmed the rejection of the null hypothesis and that people were willing to pay premium for the homes in new urbanist communities, which is in accordance with the literature reviewed. But the coefficient suggests a higher premium percentage than what was mentioned in the literature. Some of it can be attributed to the age of the homes as they are newly built. In addition, since all the new urbanist homes are from one subdivision, its location characteristics may also explain the differential in the price.
Summary
Some of the characteristics of new urbanist communities are small lots containing houses with front porches set close to the street; neighborhoods with plenty of public space; controlled landscape; and pedestrian friendly streets. It stands out from competing conventional housing stock. New urbanist communities have gained popularity either due to the changing demographics or change in taste. Despite this growing popularity, not much research is done to find out the market acceptance of such communities.
For the purpose of this research, a local housing community with the above mentioned characters is identified. Hedonics analysis method is used with sales price as the dependent variable and “new urban” (proxy for new urbanist characters) as one of the independent variables along with other independent control variables.
Results
The stepwise-stepwise regression model has adjusted R square value increased with addition of every variable and regression model explained 88% variation in sale price (adjusted R square = 0.880). The model selects other six independent variables which are also statistically significant. The coefficients for these variables show strong relationship to the sales price as expected at a significance level (min. 0.000**, and max 0.003**), indicating very less chances of accepting null hypothesis. The variable of interest ‘new urban’ show strong positive relation to the sales price ($47,000) with t static (8.7) at significance level (0.000**).
The residual plots for standardized residual to adjusted predicted value do not show any violation of linearity and statistical independence assumption. Little flared pattern is observed mainly due to some higher sales prices in newer homes. It can be attributed to the homes built when the real estate markets were unrealistically bullish and certain custom built homes. Since number of such transactions is low, adjusted weight method technique was not applied. Also the outlier identified show relatively low standard of error i.e. less than four.
To make sure the price differential is not due the age of these communities (newly built) and desired school district, three dummy variables to describe age and one for school district are manually entered. Of those, the model only showed one of the variables i.e. homes less than eight years in age, has statistically positive relation to the sales price ($18821) with t statistics of (2.82) at significance level (0.005**).
Conclusion
The results confirmed the rejection of the null hypothesis and that people were willing to pay premium for the homes in new urbanist communities, which is in accordance with the literature reviewed. But the coefficient suggests a higher premium percentage than what was mentioned in the literature. Some of it can be attributed to the age of the homes as they are newly built. In addition, since all the new urbanist homes are from one subdivision, its location characteristics may also explain the differential in the price.
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