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.

School Distance and Home Prices

The rationale for this study is to establish a negative correlation between property values and the distance from public schools. Many determinants are used when evaluating the price of a home. This study uses stepwise linear regression to establish school distance as a determinant of the value of a piece of property. Some questions that can be addressed through this statistical study are: Does distance from a public school affect the value of a home? How much (in dollar terms) is the value affected? Is there strong enough statistical evidence to indicate a negative correlation between house prices and school distance? These questions can, and are, answered through this statistical study.

The results of the stepwise-stepwise linear regression analysis confirm the hypothesis that home values are negatively correlated with distance from schools. The variables that were entered/removed from the model because they met the level of significance threshold were area, number of bathrooms, four miles, and days on market. The fourth model produced included area, number of bathrooms, four miles, and days on market as the predictors of sales price. The r square for the fourth model was .705 and the adjusted R square was .702. This means that the fourth model produced explains 70.5% of the sales price. The adjusted r square decreased by .03, which is a solid indicator of confidence in the numbers. When looking at the ANOVA table, the F statistic for the fourth model is 202.420 with a p-value of .000. The F statistic is very high and has a very low p-value, thus indicating strong statistical significance. The t-statisitc for the independent variable of four miles in the fourth model is 3.175, which indicates a model that possesses considerable strength and aptness. The coefficients of the fourth model show that a home located three to four miles away from the appropriate high school capitalize a $24,281.52 discount into the price. When looking at the collinearity statistics, a variance inflation factor (VIF) for the fourth model’s variable of four miles is 1.040. This is very low and indicates that there is no multi-collinearity occurring between the independent variables. All four statistically significant independent variables have VIF’s of 2.396 or less. The rule of thumb when determining multi-collinearity between independent variables is that VIF’s less than 10 show that multi-collinearity is not an issue in the model. Any VIF of 10 or more indicates multi-collinearity. Therefore, multi-collinearity is not an issue in the model. The variables that were excluded from the fourth model because they did not meet the statistical level of significance were number of bedrooms, name of highschool, one mile, two mile, and three mile.

Investigating the Relationship between the Quality of Middle Schools and House Values within the Birdville and Keller ISD in Watauga, Texas.

386 residential sales were investigaed in Watauga, Texas between March 1, 2008 and June 5, 2009. Data was analyzed and missing data points were created by associating with nearby addresses. School rating information was not part of the original MLS dataset. These rating categories were obtained via the Texas Education Agency website. The scope of the investigation was narrowed to include only middle schools as this had the most information per rating category. A preliminary model was constructed with property characteristics having a sufficient amount of data (greater than 20). The adjusted R-squared was 84.6% for the preliminary model. Only statistically significant variables were promoted to the final model. This reduced the number of independent variables from 23 to 9.

The final model had an adjusted R-squared value of 84.8% and showed that parents are willing to pay a 2.89% premium for middle schools rated Exemplary. The level of significance was p<0.076 which is in the gray area. Thus, although quality does matter, it is a characteristic of quality which parents consider. More research is necessary to dissect the term general term "quality" into its constituent parts. Such characteristics may be the quality of the facility, the level of parent-teacher interaction, the technologies available to students, the number of qualified teachers, etc.

Does Mineral Rights Affect Propert Prices?

The Arlington 83-I area is used in my analysis. The results show a significant "mineral premium" on sales price. A property transferring the mineral rights to the buyer increases sales price by $17523.317. This is the most persistent factor influencing sale prices.

My model is--

Sale Price = 8909.460 + 59.116 Square Feet -716.588 Age +16435.744

Total Baths + 17523.317 Mineral Rights

There is a 95% confidence that the population mean for mineral rights lies within the interval

[ 8993.477, 26053.158]

Another finding in my study is the bi-directional causality between sale price and days in the market, I accomplish this by a Granger Causality test.

Can Sellers Get a Higher Price For Their Home During the Summer?

An analysis was conducted in order to determine if there was a relationship between the time of year a house goes on the market and the sales price of a home in the east area of Dallas, Texas in 2008. The date of listing is classified into two categories; Summer Break which occurs from April through August, and School Season which is September through the following March. The study found that there was a significant relationship between time of year and sales price. Since there is a higher volume of sales during the summer time, there is a greater market where buyer preferences are able to be met and regardless of property type there was an effect. When entering an interaction variable between season and property type, the study found that single family homes sold during the months of April through August added a premium of a little over $10,000 to the price of the home. This could account for families that are more apt to move during the summer months while kids are between school.

The Effect of Foreclosure on House Prices

Abstract: The purpose of this study is to examine effects of foreclosure on house prices in the area near the Dallas Forth Worth International Airport. This study finds that foreclosure reduces property value significantly. The results indicate 32.44 percent discount on prices for foreclosed properties after controlling for other variables including size, and age of house. This evidence is consistent with earlier empirical findings.

Richardson Heights Residential Subdivision Regression Analysis:
Days on Market vs. Final Sales Price

I. RESULTS
No underlying model assumptions required for this regression analysis were violated. Each of the observations was independent. Scatterplots and correlations coefficients for all multivariate combinations were evaluated and the use of a linear function form of final sales prices was determined to be justified. The final sales price is a normally distributed continuous random variable with randomly distributed residuals.

A complete documentation of the stepwise regression is included in the reseach paper. Model 3 has the highest coefficient of determination, explaining 67.7% of the variation in final selling price. Models 1, and 2 were rejected because they have a significantly lower level of explanatory power, at 66% and 67% respectively.


II. CONCLUSIONS
The additional independent variables are both feasible and sensible to include in the model as the size and number of bedrooms of homes are known by non-statisticians to be sufficiently correlated to its final selling price (see scatterplots below). Statistically speaking, their t-statistics are significant and validate the simpler model results that size alone explains 66 % of the variability in final selling price, and coupled with more than 3 bedrooms adds another 0.01% of variance explanation.

In conclusion, the Richardson research study demonstrates that size, more than 3 bedrooms, and the days on the market does impact the final sales price.

Jerry Burbridge, Barrett Shepherd, Ali Samee

Location (second post -- question)

After taking advice from Dr. Hansz and Ramya I have developed a new model that expands upon the one previously posted. My "Location" paper is focusing on the variable: distance from a franchise restaurant. I split the distance into 4 dummy variables: B(Distance, 1/3, 2/3, 3/3, 4/3). My previously posted model had 3 of 4 distance variables kicked out for different reasons.

Following advice, I added a "Pool" dummy variable. It changed everything. Now 3 of 4 distance variable are inside the model explaining the Sales Price. The problem is that I lost .o1 R^2. The new model has a R^2 value of .777 instead of .787. Both models show strength, aptness, and follow the rules of regression.

I am inclined to choose the new model. Any advice?

The Value of a Cul-de-sac

Cul-de-sacs provide homeowners with several benefits and features. Among them is reduced traffic. This results in increased security. Also, a cul-de-sac promotes a more interactive and close knit environment. This analysis will investigate whether there is a positive correlation between being located in a cul-de-sac and home values. The data collected for this study is from 83-1 Arlington, Texas. From that data, the variables will then be compared to see if they have an effect on home prices. And then finally, houses located on cul-de-sacs will be compared to all other houses to determine if, in fact, there is a premium for the location.

With the data used, the size, age, number of bedrooms, number of bathrooms, lot size and the location in relation to a cul-de-sac were all analyzed. The variable of site location was then broken down into three variables to find significance in the model. Dummy variables were used to analyze the variables of homes located on a cul-de-sac, homes located on a dead end street, and homes located within a grid type street. The model removed the variable of cul-de-sac location due to the lack of significance in the model.

In conclusion, the data determined that there is a price premium for homes located within a dead end street compared to homes located within grid type streets. The results of the regression model explain that cul-de-sacs do not have a big significance on home sale prices. Each area of study can have different results as cul-de-sacs can be more valuable in other areas than Arlington. After conducting this analysis, it is evident that homes on a cul-de-sac do not have any extra value however; homes located on a dead end street do have a small premium over other homes in the Arlington area.

Summary Post for Final Project

The Effect of Parks on Residential Home Values
The proximity of parks, green spaces, and trails can have an impact on residential home values. The analysis that is conducted explores the correlation between the proximity to parks and the sales prices. The data comprises of sales information from area 83-1 in Arlington, Texas. The sales information along with the distances to parks has been studied and evaluated. The main focus is to determine exactly how home values are affected by amenities such as parks, green spaces, and trails.
The descriptive variables of the analysis include property size, number of bedrooms, number of bathrooms, lot size, age, swimming pools, days on the market, and the distance to the nearest park. The variable of interest in this analysis is the distance of the parks and green spaces from the residential properties. 13% of the properties are located within 750 feet of a park; 28% of the properties are located 750 to 1,500 feet from a park, 35% of the properties are located 1,500 to 2,500 feet from a park, and 24% of the properties are located over 2,500 feet from a park.
After evaluating the park distances and the other variables of the properties, there is a strong correlation in park distances to sales prices. The regression analysis explains that there is a premium of approximately $4,763 for residences located within 750 feet of a park. The analysis also states that there is a premium of approximately $1,077 for residences located within 750 to 1,500 feet of a park. For homes located over 2,500 feet from a park, the analysis states there is a discount of $1,304. The model did not analyze the variable of homes located 1,500 to 2,500 feet from parks, due to the lack of significance.

Location (Summary Post)

The paper titled Location builds the case that distance from franchise restraunts can be accounted for in the sales price. Franchise restaurants are representative of major roads, commercial districts, noise, and light pollution. The hypothesis is that there is a "sweet spot" where postive externalities are maximized, and negative exernalities minimized. The distance is between 1 mile and 4/3 miles from franchise restaurants. It is believed that this distance allows for an adaquate buffer from commercial use while still allowing for convenient access.

H0: β(Distance>1) = 0 ; H1: β(Distance>1) ≠ 0

The model that was built to control for the age, size of the home, and lot is as follows:

YiF= β0 + β1(square feet) +β2(acres) + β3(age) + β4(Distance 4/3) +β5(Distance_between 1/3 and 2/3) + ε

In this model, Distance was turned into a dummy variable dividing 1.3 miles into four portions. Other portions were tested with no success. By splitting Distance into 1/3 portions, the explanatory value of the variable was maximized.

Several variables were thrown out of the model due to statistical irrelevance or issues with correlation. These include the dummy variables for beds and baths, school district, and Distance 1/3, and Distance 3/3. By removing these variable the model was stronger and more apt.

The F-Statistic measures the model's overall strength. The result is: 243.907
The R^2 Statistic is a measurement of confidence. The result is: .787
The Adjusted R^2 controls for the loss of df. The result is: .784
The t-statistics are listed in the order of the model: 2.317; 26.623; 2.951;
-5.453, 2.044; -1.653

After looking at the residuals (Histogram, Normal P-Plot of Standardized Residuals, and Scatter plot) they appear to be normal, linear, homoskedastic, and the kurtosis looked great. Multi-collinearity did not appear to be an issue either.

The end result with regard to the Distance 3/3 variable's B-coefficient is:

P(9,107.512 < B < 26,555.36) =.95


In conclusion, the null hypothesis is rejected.

Heteroskedasticity

Useful Links
White's Test
http://pages.infinit.net/rlevesqu/Syntax/RegressionRepeatedMeasure/WhiteTestStatisticsAndSignificance.txt
http://www.geocities.com/rlevesque/SPSS/HeteroscedasticityTestingAndCorrectingInSPSS1.pdf

Weighted least Squares
http://faculty.chass.ncsu.edu/garson/PA765/wls.htm

Article on New Urbanism and Property Values

I came across the following article on new urbanism and property values.

Song,Yan andGerrit-Jan Knaap, BNewUrbanism and HousingValues:ADisaggregate
Assessment,’’ Journal of Urban Economics, 2003, 54:2, 218–238.

I got reminded about Yogesh Patil's final project. Thought this might help him in his literature review.

Informal UTA-Fort Worth Reception Details...

I had no idea this was going on until Dr. Hansz mentioned it in class. Thanks Dr. Hansz. I'm placing the details here just in case someone else "didn't get the memo". I'm going and I hope to see some familiar faces there...

**********************
MSRE Students:

Just in case you did not receive the e-mail from another source, you are invited to an informal reception for the UTA Real Estate Program in Downtown Fort Worth - Saturday, July 11 from 6:30 pm - 7:30 pm in the "Gallery 76102" at the UTA Fort Worth Center - 1401 Jones Street, Fort Worth, Texas 76102. This informal gathering is open to former, current, and prospective UTA Real Estate Students, Faculty, and Friends. The purpose of this gathering is to promote networking and socializing opportunities among the attendees as well as giving everyone the opportunity to see the new home for the Graduate Programs in Real Estate at the UTA Fort Worth Center in Downtown Fort Worth. At the conclusion of the reception at 7:30 pm, there will be an opportunity for those interested to move to another Downtown Fort Worth venue for dinner, drinks, and further interaction with other attendees. In order to adequately order appetizers and drinks, please send your RSVP to Fred Forgey at forgey@uta.edu before Saturday July 11th. You are welcome to bring your friends, spouses, significant others, etc.
***********************

The Effect of Noise on House Prices

Abstract: The purpose of this study is to examine the effects of noise level on house prices of the area near the Atlanta Airport. We find that noise level and house prices are negatively related. The results indicate that house prices discount $10,505.45 for noise level at 40 after controlling for size of house, age of the house, and number of months old from the first house sale in sample. This evidence is consistent with earlier empirical findings.

SPSS 17.0 Gradpack update

FYI...There is a free update for those who are using SPSS Gradpack version 17.0. The update upgrades version 17.0 to version 17.0.2 and fixes some internal bugs as well as import/export problems, table display problems, among others. On the SPSS toolbar, go to Help - Check for Updates. Then follow the directions.


Eduardo (Ed) Torres-Valdez

Exam 1 Scores Posted to WebCT

Exam 1 grades have been posted to WebCT (including descripive statistics). We will review Exam 1 in-class on Wednesday.

Exam 1 on Monday (yes, this Monday!)

Exam 1 this monday will have around 10 multiple choice style questions and 4 to 5 essay/short answer/calculation questions.

All you need to bring is your pencils, basic calculator, and Scan-tron (if you have one) to the exam. This is a no notes, closed book examination.

If you will be required to use a z or t table, I will provide copies of the tables that we have been using for class.

Richardson ISD-what's it worth?

Richardson ISD: what’s it worth? A comparison of residential sales prices by school district and municipal jurisdictions in a North Texas suburban region.

The realtors in the Richardson ISD school district proudly display property for-sale signs with RISD logos attached, indicating to potential buyers that a house location within RISD is a strong selling point for many buyers. RISD administration boasts many district awards over the years as one of the strongest performers in public education excellence in Texas and in the DFW metropolitan region. From the evidence suggested, residential properties for sale in Richardson ISD would seem to command a selling price premium over other comparable properties in adjacent neighborhoods located in different public school districts.

This study assumes that families with school-age children would prefer to live in a public school district with higher education quality as measured by state education rating standards and other public measures of school performance. Their preference for a better school district will be measurable in their home-purchasing decisions as a price premium paid for comparable housing and property amenities in one school district versus others nearby. Other categories of home buyers, such as “empty nesters” or “DINK’s” may not be as attracted to the value of a school district, and would not be willing to pay a premium for a property more typically demanded by these segments of the market (i.e., smaller homes, townhomes, condominiums, duplexes.)

Richardson ISD covers geographic areas of the City of Richardson, City of Garland, and the City of Dallas. However, each of these three municipalities is also served by at least one other local independent school district: Plano ISD, Garland ISD, and Dallas ISD, respectively. This study hypothesizes that residential home sales in each municipality served by RISD will command respectively higher prices than comparable homes in size, age and amenities located in the same city jurisdiction but in different ISD service areas. The study will also investigate whether there are any moderating relationships between ISD serving the home and other features of the home itself, such as size, age, and number of rooms. The house size, age and number of rooms are hypothesized to be proxies for the demographic segment of the housing market served (e.g., nuclear family with school age children vs. singles, couples, and retired.)

Confidence Interval Homework Answers

Ok, I can't write the confidence intervals the correct way, but here are some check figures for questions 1 and 2 only.

1. P($82,864.02 le U ge $84,902.64)=.90

2. P($78,916.24 le Xnew ge $88,850.42)=.90

What did you get for problems 3 and 4 (skip 5)? Please post your answer by commenting to this post.

Significance of Seller Paid Cost in Residential Real Estate

When selling residential property, there are several techniques used by the seller to make the property more attractive to potential buyers. One of those techniques is the seller offering to pay for some of the costs (including closing costs, repairs, etc.). From the sellers' point of view, it is commonly given that a) the final/net funds received from the sale and b) the least amount of time the house stays on the market, are what matter. From the financial aspect, allocating some funds for closing costs, repairs, etc. , which otherwise is expected to be lost in discount needed to arrive at final sales price, is a well known practice. The seller, after paying for some of the costs, expects to recover those costs at the end, or at least expects a positive correlation between the amount of paid costs vs. the final sales price. From the time aspect, it is expected that such practice will also result in the house being sold faster than otherwise. My hypothesis is that there is no significant correlation between the amount of seller paid costs (% of asking price) vs. the final sales price (% discount applied to asking price in arriving at the final sales price) or between the amount of seller paid costs (% of asking price) vs. the number of days a house stays on the market.

Conservation/Historical Districts and Heighborhood Groups

Oak Cliff was developed as an elite residential area in the late 1800's. For many years the area competed with Highland Park as the top enclave. Beginning in the 1960's the area experienced a rapid deterioration and became a symbol of urban blight. Recently, that has changed and the area is experiencing a tremendous amount of reinvestment in residential (and commercial) properties. There are many catalysts for this redevelopment. My hypothesis is that homes in the 26 various conservation/historic/neighborhood districts sell at a premium versus homes outside of officially designated areas. Furthermore, the older the neighborhood plat, the higher the sales price will be.

Alex Papavasiliou

New Urbanist Communities: Does it add value?

Recently there are some residential subdivisions are developed on new urbanism principals in a typical suburban locations in the DFW metroplex. Whether it is life style preference, change in demographics or scarcity of the land, more and more such communities are being proposed.
But eventually if these types of developments bring any premium to the home prices compared to the traditional housing, will decide if this trend is here to stay.

This study focuses on the comparison and analysis of the home prices in such sub division to the home prices for traditional homes in the surrounding area.

Listing-Sold Price Change VS Days on Market

This project will study the relationship between the difference of listing - sold price and the number of days the property has being listing on MLS. It will use the default data (Arlington area 83-1) as the project database. The study will focus on 4 situations: 1) Home sellers value their houses too high, the properties will be listing on the market for many days. 2) The houses are overvalued but sold very soon. 3) Home sellers set the price lower than the market value, houses are sold very quickly. 4)Houses are being undervalued and buyer are hesitate about the true value of the properties and make them listing for long time. The study will test if these trends or some them are believable in the area of Arlington. Also, the report will find out what factors have the main effect on the listing days as well as on the price change.

The Impact of Tear-downs and Rebuilds at White Rock Lake

White Rock Lake (areas 12-7 and 12-8) is a very unique and desirable location within Dallas County in terms of having waterfront property in close proximity to downtown, SMU, and various other shopping and dining amenities. Its per square list prices often exceed $150-200 per square foot which is comparably high within the greater Dallas area (e.g. Plano in Collin County averages closer to an average price of $95 per square foot)

This area also contains a battle for the character of its neighborhoods as it has been the source of frequent redevelopment efforts in recent years. These efforts have largely been of the tear-down and rebuild variety. The existing housing developments were created primarily in the decades of the 1940s, 1950s and 1960s with predominantly similar architectural designs and proportions. However, the newer houses that have been built within these neighborhoods have varied widely in terms of architecture (e.g. two-story modern designs reside in the middle of a street with single story mid-century ranch styles) and in size as the newer designs tend to dwarf the size of its neighbors.

While this area is a desirable location within the city, my study will analyze the effect of tear-down/rebuilds on the sales prices of neighboring properties that have not undergone significant renovation within the last ten years. This will be done by examining sales data associated with the year built, mean size of houses that are less than 10 years old versus 10+ years, and the mean number of bedrooms/baths between the two age categories. In addition, I will look at the magnitude of this effect on sales price by considering proximity to the lake itself by geographic distance.

Has the Trinity River Project Increased the Sales Prices of Surrounding Houses?

The development project underway called the Trinity River Corridor Project, which is located along side Downtown Dallas, has made a huge impact on the surrounding neighborhoods. This project is supposed to cover about 20 miles of the Trinity River, which will include new roads, hike and bike trails, wetlands and lakes, and additional park and recreational facilities. The project is set to have many benefits including increased flood protection, traffic congestion relief, and neighborhood revitalization. North Oak Cliff is one of the areas that has been highly impacted by this project. Although the neighborhood was once an area seemingly in decline, it has had a great comeback in the last few years. The main attraction in North Oak Cliff is the Bishop Arts District. It includes over 50 local merchants, restaurants, boutiques, and services.

There have been many new developments in the past few years in this area and I think that this has definitely boosted the sales prices of surrounding homes. I would like to focus my study on the sales price and the area in which the property is located in. I would like to see if sales prices on homes with a close proximity to the planned Trinity River Project are higher than the prices on homes that are farther away.

Richardson Heights - Days on Market

Richardson Heights is a residential neighborhood in northwest Richardson. The area of interest in our study, are homes that fall north of Beltline, south of Campbell, east of Coit, and west of US-75. The single family detached homes in this area were generally constructed between 1955 and 1965. Despite the homes being approximately 50 years of age, several realtors have told me that this area is a "hot" market. The major factors they point to is solid public schools (Richardson ISD in 2005 had 50% or more of their schools rated "exemplary" or "recognized"), affordable homes (generally between 130-180k in this area), and as the old saying goes, location, location, location! Homes in this pocket of Richardson have access to Dallas North Tollway, President George Bush Hwy (190), US75 (Central Expy), and I-635 all within a 3 mile radius.

We'd like to test the correlation between DOM (Days on Market) and the sales price. Our contention is that homes whose DOM far exceeds the median DOM for this area end up settling (closed sales) at values below the median for the area.

Ali Samee, Barrett Shepherd, Jerry Burbridge

Why Choose Arlington ?

Arlington is the 7^th largest city in Texas and 50^th largest city in United States. The city has recently become the home of the Dallas Cowboys Stadium which has increased the popularity of this area . Consequently an increase in demand is expected. But question arises will the falling trend in the real estate market survive the bubble and consumers will be again comfortable with home buying in the Tarrant County. Are there any increase in house sales and consequently any increase in house prices?

Surveys of the properties sold in 2008 and 2009 shows a lot of properties allow the owners to retain mineral rights. There can be also a possibility the home-owner just purchased the property to get access to the mineral rights and not interested in living in Arlington. If the house is leased out and the owner lives somewhere else then we can say mineral rights is the only driving factor for increase in sales.

As we know other than the type of location house prices depend on a lot of attributes. The year of construction is often looked upon by home buyers. I include a dummy variable in my work. A assign a dummy variable of 1 if the house is constructed before 1970 and 0, otherwise.

The Arlington Area 83-1 is used in my study and my purpose is to look into the factors affecting house prices and come up with suggestions which can help sellers to re-model their properties to suit the demand of buyers.

The effect of foreclosures on house prices

Most of the economies in the world are facing a serious challenge in the history. Real estate market's meltdown in the US is partly blamed for the current economic crisis. Housing crisis starts with declining housing prices in the US. Although, the housing prices starts declining from the early 2006, the problem exasperated in the second quarter of 2008 when housing prices dropped substantially. House prices fall is continuous and the housing market has not experienced any stability yet. Substantial drop in housing prices has caused significant increase in foreclosures around the country. There are several factors contributing to the collapse of housing price including size, age, noise, different features of the property. There is a widespread concern about the impact of the foreclosed house on housing price in the neighborhood. There is also a general consensus among common people that foreclosed housed are cheaper to comparable properties. In this context, this study investigates if there exists relationship between housing price and foreclosures. Are foreclosed properties underpriced to comparable properties?

The purpose of this study is to examine the influence of foreclosures on housing prices of the area near the Dallas Forth Worth International Airport. This study focuses on measuring the effects of foreclosures on house prices. Are foreclosed properties are undervalued? This paper investigates this question within a dynamic model that addresses the other factors influencing house prices. There are enough evidences that there are market wide systematic effects on housing prices. In this context, this study explores the other factors other than market conditions and the local economy in this model. This study develops a dynamic model of house market at a local level to estimate the influence of foreclosures on house price. House price is a function of several other factors. Therefore, this study concentrates to same local area which share common characteristics. Modeling housing market at the local area with common characteristics is expected to be helpful in identifying the relation between foreclosures and house price. In other words, the study on the factor affecting house prices at a local level will improve empirical finding which helps to gauge the effect of foreclosures on house price.

This study is organized as follows. Section II reviews the existing literature on the relationships among foreclosures, home prices, and other housing variables. Section III provides the detail information about the sample source and selection process, variables used in this study, estimation techniques employed. Section IV presents the estimation results of the study. This section also includes results of several robustness tests. Finally, Section V concludes the finding of the study.

Best Time to Sell a Real Property

The sale price of a real property may be influenced by which part of the year the sale occurs. Generally, Spring and Summer are the best times to relocate and purchase a house. Hence if the sale is made in Spring, there is a general trend in an increase in inventory which could make it more difficult to sell than at a time like November when inventories are coming down. Also, if the home is a single family residence, then a sale in Summer may increase the price per square foot because families tend to relocate in tandem with the school year. If the home is a single story with room for an RV, the most likely buyer might be a retired individual or couple, in which case there is no connection to school years, making the off season a great time for them to be looking.

I would like to investigate the relationship between the timing of a sale and the price per square foot of the real property in the Arlington Area 83-1 near UTA and seek to come up with the factors if any that affect this relationship.

Favorable Schools and Home Values

I want to investigates the correlation between favorable schools and home values located within the school's district. Are home values affected by the proximity to the school? This project is designed to find data and hopefully answer this question. If you look in Dallas, you can see one of the best public schools is Highland Park. And the surrounding homes located in Highland Park and University Park are priced very high, which I feel is impacted by the location within the Highland Park high school district. I want to look in Arlington and try to find the best schools and determine if the surrounding home values are affected by the proximity to the school. 

The Value of Higher Education...

By now, you’ve most likely heard that “Location! Location! Location!” is the most important factor in real estate. The determination of this ideal “location”, from an investor’s point-of-view; is that place where property value is maximized. In this case, we are concerned about maximizing the value of a residential property (single-family residence). This ideal location could be determined by a number of factors, such as:
1. Proximity to food and entertainment;
2. Proximity to places of higher education and libraries;
3. Proximity to sources of noise;
4. Access to roads and number of road access points within a neighborhood (i.e., short-cuts);
a) Conversely, points where traffic is limited may also be associated with adding value.
5. Proximity to recreational areas (parks, lakes, biking and walking trails, etc.);
6. Neighborhood characteristics contribute to the value of the property. Characteristics such as:
a) Width and extent of paved sidewalks;
b) Number of old-growth trees within the neighborhood;
c) Nearby man-made or natural streams/canals;
7. Building characteristics also contribute to the value. Characteristics such as:
a) Wrap-around porches;
b) Single or multi-pitched roofs;
c) Number of bedrooms and bathrooms;
d) Basements;
e) Pools;

And the list goes on…

This paper will discuss how the value of a home is affected by the proximity to a place of higher education (elementary, middle, high, college, or university). Which is most desirable, living by a school (walking distance), living near a school (long walk, short bike, or short drive), or living away from a school (drive only or take the bus)? The quality of the school system will also be taken into consideration. Even if the prospective homebuyers do not have children, it would be prudent for them to research the closest schools before purchasing a home. Good public schools are always in demand and in turn affect real estate values. Communities with high scoring school districts appreciate more, or in this market, depreciate less than communities with low scoring school districts. For many prospective home buyers with children (or those who are planning to have children), the quality and reputation of the local school system may be as critical to their buying decision as the appeal and location of the home itself. The quality and reputation of the school will affect the home's value, not only when bought, but also when sold.

Topic Posts

I will try to post comments on your Final Paper Topic posts this week. It is nice to see what you are thinking about. Most topics appear to be 'do-able' (this is a technical term for you should have the data to get it done by the end of the semester).

If anyone would like to add a comment to a comment, please feel free.

How are you doing with SPSS? If it makes anyone feel better, I am getting hammered with Argus this week!!

The Value of a Cul-de-sac

The Value of a Cul-de-sac investigates the correlation between residences located on a cul-de-sac to its home value. Essentially, a cul-de-sac is a dead end street with a turnaround at the end for cars. There are many benefits to living on a street with a cul-de-sac. For example, there is less car traffic which leads to less noise. As a result, there is less crime as there are fewer escape routes. This creates a sense of security and piece of mind. There are many streets in the Arlington Area 83-1 that end in a cul-de-sac. This analysis will investigate whether there is an increase in value of the homes located within a cul-de-sac compared to all of the other homes.

Affects of Neighborhood Amenities

Neighborhood amenities such as parks, playgrounds, green space, walking trails, and community swimming pools are features that are generally desired by residents and can affect the property values of the homes nearby. The homes that are in close proximity to these amenities are more desirable and thus more valuable to a large number of households. Recreational activities such as walking, running, biking, swimming, and other sports activities can be enjoyed by many different age groups. The affect on value of homes that are conveniently located near parks, walking trails, and other amenities will be explored in the analysis.

Road and Real Estate

Residential real estate serves members of society though their entire lives. Young adult people typically look for single bedroom apartments or possibly two bedrooms when looking for roommates. When people get older, and start a family, three to five bedrooms may be necessary to serve the growing needs of the family. Real estate emphasizes “Location, Location, and location.” Young adult people are concerned how easy it is to get to the highway for work. However, families may choose small sized roads (less than four lanes) because they want a quiet and safe neighborhood for children. The paper is focused on how road sizes (avenue, street, blvd, lane, and highway) influence the numbers of bedrooms.

Road sizes play different roles for different people. It is important to understand the relationship between these road sizes to better understand the target market when selling residential real estate. Marketing dollars will be spent more efficiently if the target market is known. This is also important for developers to help them understand how infrastructure can play a role in promoting the sale of their property.

Venue Versus Value

Venues Versus Value discusses the relationship between the distance of different types of food venues and the surronding real estate value and sales volume. When driving down many streets (MLS area 83-1: Arlington Texas) it is evident that there is an eclectic array of food venues, and residential subdivisions. Does the presence of food venues add or subtract from the property value and sales volume inside thier supporting subdivisions?

Food Venues offer convenience and entertainment; their presence has an effect on the residential property that surrounds it. This paper shall initially propose that the distance between food venues is directly related to the value and sales volume of the aforesaid. The greater the distance: the greater the value, and the greater the sales volume. It is important to understand the relationship between specific commercial activities that serve the community for purposes of zoning, planning, and investing.

Online Raw Data Legend

I thought I'd share this with everyone. It is a link to an Excel workbook that helped me decipher ALL of the cryptic notations within my raw data. Choose the "Look Ups" tab in the Excel workbook and use the "Ctrl+F" function to find the notation that is of interest. Once you know what the notation stands for, return to your raw data and use the replace option in the "Ctrl+F" dialog box to replace the notation with a more meaningful phrase. Example: Replace "EXFGST_QUA" with "Exterior Features, Guest Quarters"

www.ntreis.net/documents/MLSOnSite_2722007153057.xls


Eduardo (ED) Torres-Valdez

variable input form link on web

Here is the variable input form as a web link....I found it while researching and thought I'd share it with everyone.

http://www.ntreis.net/documents/Forms_58200522131.htm


Eduardo (Ed) Torres-Valdez

SPSS Must Dos

You need to figure out the following procedures in SPSS for both the "noise" papers and final papers.   Good luck this week!

*Input data by either loading an Excel file into SPSS or cut and pasting data directly into SPSS (when cut and pasting remember that you need to setup the SPSS column type before you can paste the data).

*Run a basic descriptive statistics table for all variables.

*Run frequency tables or charts for all variables.

*Run a correlation matrix.

*Run simple xy plots to visualize simple bivariate relationship between variables.

*Learn how to recode qualitative data into dummy (indicator or 0, 1) variables.

*Run a regression analysis using Enter and Stepwise commands.

*Run various diagnostic test on regression residuals.

*Create confidence intervals and prediction intervals for regression coefficients and estimates.

WebCT Setup

WebCT is now setup for the course. Please remember that we only use WebCT for posting grades. Never contact me through WebCT e-mail. Always use reae5350@gmail.com. Please log-on to your WebCT and verify that you are enrolled in the course. Thank you!

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Watauga, TX Data...

Thanks for your help. I received the Watauga data just fine Dr. Hansz.

Eduardo (ED) Torres-Valdez

Default Data Reposted (with many more fields)

Just a quick note to let you know that I reposted the default data (the area 83 in Arlington) to include many more fields. The default data should be go to go. If you find any problems, contact me or bring the issues to class.

Internet resources for learning SPSS

Here is the website from UCLA I was talking about to learn SPSS online in the first class.
http://www.ats.ucla.edu/stat/spss/

Videos here: http://www.ats.ucla.edu/stat/spss/notes_old/default.htm and http://www.stat.tamu.edu/spss.php

SPSS Student Version Limitation...

The Student Version of SPSS can export results to a Microsoft Word or Excel file. Export to PowerPoint is available only on Windows operating systems and is not available with the Student Version.

Eduardo (ED) Torres-Valdez

Text Chapter Titles

If you are matching up chapters with another text, I though the chapter titles would be helpful.  Please see below.

Chapter

1   Introduction and Descriptive Statistics

2   Probability

3   Random Variables

4   The Normal Distribution

5   Sampling and Sampling Distributions

6   Confidence Intervals

7   Hypothesis Testing

8   The Comparison of Two Populations

9   Analysis of Variance

10  Simple Linear Regression and Correlation

11  Multiple Regress

(we will stop at chapter 11)

SPSS Student Version will Be Fine

The SPSS Student version will accommodate 1,500 records and 50 variables which will be more than adequate for our purposes. If you find a good deal on a student version, it should work fine.

Dr. Hansz's Summer Office Hours

My office hours will be from 2 to 4 PM on Mondays and Wednesdays. Please note, I will not have office hours on June 15, 17, 22, and 24.

Class Blog

Please use this blog to ask questions (to me or other students) and feel free to post information that you think that the class might find helpful.  Thanks!

Affordable SPSS Gradpack software site....

I bought the 6-month, SPSS 17 Statistics for Windows Gradpack with Advanced Statistics, Regression and Amos for only $79.99 at:

http://estore.onthehub.com


Eduardo (ED) Torres-Valdez
817.733.4478 (cell)

Class Website Ready to Go for Summer 09 8-Week Session

To find the course homepage, please go to my faculty website www.uta.edu/faculty/hansz and click on the link for REAE 5350.  Make sure you download the syllabus, print-out, and bring to our first class on Wednesday, June 3rd at 5:30 PM.  You will also probably want to bring a print-out of the class notes (also on the course homepage) to the first class.

Class Text for Summer 2009

The recommended textbook is at the UTA bookstore.  The book is titled:

Complete Business Statistics 7th edition by Aczel and Sounderpandian (McGraw-Hill Irwin).

However, there are many good statistics textbooks on the market and I have used a variety of textbook for the course in the past.  If you can match the topics and material that we will cover to another statistics textbook, you are welcome to use it at your own risk.

I would like to suggest two other books you might consider looking at, especially if you have a fear of statistics and regression:

Statistics for People Who Think They Hate Statistics 3rd edition by Salkind (Sage).

Regression Basics 2nd edition by Kahane (Sage).

Also, there is a Statistics for Dummies book that isn't a bad read but I didn't want to insult anyone.

Finally, you will need access to the statistical software package called SPSS.  The University has one computer lab with SPSS and the BookStore and UTA Software Store sell student versions of this software.  Students have also found statistics books and software on the Internet.