Final Post

Research paper and final class grades have been posted to WebCT. Have a good Fall semester!

Reminders

The final project deadline is Wednesday at 5:30 PM (paper and electronic submission).

Final blog post were due yesterday...

Please return all borrowed books on Wednesday (especially Dr. Hayunga's books!).

Big Blue is buying SPSS Inc. this morning...

Tuesday, July 28, 2009: IBM Corp. (NYSE:IBM) announced Tuesday morning that it is buying predictive analytics software maker SPSS Inc. (NASDAQ:SPSS) for $50 a share in cash, or about $1.2 billion.

Pools add value to homes

The goal of this study is to build a statistical model to capitalize swimming pools into housing prices and find the relationship between swimming pools and the values of residential properties. The data for this study comes from house transactions of Arlington area-83-1 near UTA.

All of the past studies found in the Literature Review section of this paper have found positive correlations with swimming pools. Pools do tend to add value to a home — about 7.7%, according to National Association of Realtors statistics. Methodologies and results from these past studies supported that the effect of swimming pools has a significant importance on property values. Therefore, the hypothesis of this paper is pools add value to homes.

The regression model has R square equal to 0.734 which means that 73.4% of the data is explained by this model. This number is confirmed by the Significance F change and Durbin Watson which is equal to 1.963. (very closed to 2) The model select seven independent variables. In the T statistic analysis, acres, Square ft, ages, and pools have significant level under 0.05. In 95% confidence Interval for B, the value of pools is between 2897.636 and 25447.888. In addition, In the ANOVA analysis, the F-statistic is equal to 131.347 with significant level. When checking multicollinearity, the VIF falls into reasonable range and collinearity diagnostics points out there is no multicollinearity problem. Generally, the Histogram graph looks fine. However, the scatterplot graph has homoskedasticity problem. Therefore, the natural log regression may help correct the flaw of the model. This approach considers a linear relationship by log-transformed dependent variable.

The purpose of this paper is to find the correlation among variables. The author of the paper followed the 5 steps of regression analysis beginning with looking at the data and ending with analyzing. If everything is followed correctly the ending analysis should produce valuable confidence and prediction intervals that can be used in structural modeling. Upon using the SPSS, the outcome of the analysis has shown that there is a positive correlation between the pools and sales prices.

Conserving History

The focus of my analysis was to determine if houses located within a conservation or historic district, in Oak Cliff, command a higher selling price than homes outside of a designated area. To complete this analysis 459 sales were analyzed with a hedonic model over a one year time period. Using the reported sales price based on MLS data, the hedonic model was able to explain approximately 70% of the final selling price. However, there was potentially an issue with heteroskedasticty based on the residual plot. Seeking to mitigate this issue, I used the natural log of the sales price, effectively converting the quantities to percentages. This increased the explanatory power of the model by 6%. Using the natural log method, houses within a conservation or historic district command roughly a 40% premium to similar houses outside of an officially designated area.

New Urbanism: Does it add value?

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.

Best Time to Sell a Real Property - Results

The purpose of this study is to examine if the sale price of a real property may be influenced by which part of the year the sale occurs. Generally, it is perceived that spring and summer are the best times to relocate and purchase a house. Hence there is an expected increase in demand during this part of the year that would possibly increase the price. This study looks at the sales data from Arlington Area 83-1 near UTA for the year 2008 to examine this relationship. The study does not find any significant relationship between the property prices and seasonality.

The study further extends the hypothesis by looking at the interaction between seasonality and the property type and their combined effect on property prices. Single family residences are in high demand during late spring and summer as families tend to relocate in tandem with the school year. The study interestingly finds significant relationship between property prices and the interaction between seasonality and property type. The results indicate that for single family residences selling in spring and summer, the property sells for a premium of $6808 after controlling for other hedonic variables like size, age, etc.