DEMAND PROJECT-Final

S T U D Y O F R E A L E S T A T E P R I C I N G
OPRE 6377
DEMAND AND REVENUE MANAGEMENT

PREPARED BY:
GHAZALEH HASSANZADEH
GOLNAZ SHAHFIPOUFARD
HUZAIR TIRMIZI
NITIN MAURYA
TANU AGGARWAL

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Table of Contents
1. Objective ......................................................................................................................................... 3
2. Introduction .................................................................................................................................... 4
2.1 Housing Market in United States ................................................................................................................................................... 4
2.2 Housing Prices Trend in Dallas Area ............................................................................................................................................ 4
2.3 Real Estate Industry in Collin County .......................................................................................................................................... 6
2.4 Future of house pricing in real estate market .......................................................................................................................... 8
3. Demand Drivers ............................................................................................................................ 10
3. Research Methodology .................................................................................................................. 11
3.1 Hedonic Methodology ...................................................................................................................................................................... 11
3.2 Role of Zillow’s Zestimate .............................................................................................................................................................. 12
3.3 Analysis Steps ...................................................................................................................................................................................... 13
4. Regression Analysis ...................................................................................................................... 14
4.1 Data Description ............................................................................................................................................................................ 14
4.2 Regression Model Building ........................................................................................................................................................ 14
4.3 Model Interpretation ................................................................................................................................................................... 15
4.3.1 Analysis of Variances (Table 1) .................................................................................................................................................. 15
4.3.2. Overall model fit (Table 2) .......................................................................................................................................................... 16
4.3.3 Parameter Estimates (Table 3) .................................................................................................................................................. 16
4.3.4. Predictor Effects of the Model .................................................................................................................................................... 17
4.4 Using the Model to estimate a house value ............................................................................................................................ 17
5. Estimation in the Real world ......................................................................................................... 18
5.1 Comparison between leading websites ................................................................................................................................... 18
5.2 Accuracy comparison between Zillow and Trulia ............................................................................................................... 19
6. Observations and Conclusions ...................................................................................................... 20
7. Future Scope ................................................................................................................................. 20
8. References ..................................................................................................................................... 21
9. Appendix ...................................................................................................................................... 22
9.1 Data .......................................................................................................................................................................................................... 22

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1. Objective

The focus of this project is to understand the dynamics of pricing a house in Real Estate
industry. Pricing of a house is dependent on various variables (external and internal), for e.g.
No of Bedrooms, Area, Location etc. As a part of this project we will be studying major factors
that can be quantified and affect the pricing of a house:
• Price
• No. of Bedrooms
• No. of Bathrooms
• Lot Area
• Sq. ft.
• Age
We will try to find a relation of the above factors with Price of house using Regression
Analysis. Finally, we will formulate hypotheses to validate the scope and findings of our
project by estimating the price of a house currently on sale.

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2. Introduction
2.1 Housing Market in United States
Housing market in the U.S. is characterized by what is called "boom-bust" cycles. In such a
cycle, housing prices increase for a time, sometimes steeply, and then drop. U.S. housing
prices are still recovering from the housing bust that began in 2006, and prices are dictated by
supply and demand factors [1].
Generally, supply and demand broadly affects prices for goods and services. For instance, if
one is looking to buy a home in a high-end neighborhood, the supply of good homes within
that neighborhood may be limited. When there is a limited supply of houses in a
neighborhood and numerous buyers interested in those houses, sellers can set higher prices.
Considering a seller's housing market, there are, on average more potential buyers than the
available supply of affordable houses [1].
Housing prices in individual markets may not necessarily follow downward trends of housing
prices or might recover more quickly. For instance, as of December 2012, Los Altos, California,
featuring an average listing price of $1.7 million for four-bedroom, two-bath homes, has
shown the effects of the U.S. housing market bust relatively well [1]. By contrast, the average
listing price for a home in Redford, Michigan, was almost below $60,500, according to a
November 2012 ABC News article.
Economic slowdown in 2006, took the U.S. housing market to bust. As the rate of
unemployment increased, mortgages became hard to afford, and many homebuyers defaulted.
Consequently, lenders began suffering huge losses on mortgage loans they had given. To react
to mortgage defaults, lenders started tightening credit, thus decreasing the number of eligible
homebuyers and driving down house prices. Although there were millions of houses available
for sale, eligible buyers were scarce which depressed listing prices even further [1].
2.2 Housing Prices Trend in Dallas Area

The Dallas area leads the country in home price gains. Compared to last year, median home
sales prices have been more than 10 percent higher in 2015 summer. In a dozen Dallas-area
neighborhoods price hiked up to 15 percent or more, according to a midyear analysis of the
North Texas home market based on data from the Real Estate Center at Texas A&M

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University. Real estate agents point out that the demand for housing and the prices are fed by
relocations resulting from job growth. Demand is especially high for more affordably priced
homes, which are in short supply in the Dallas area [2].

Fig 1: Housing Prices Trend - Dallas Area - 2015

Thirty of the 45 Dallas-area residential districts which “The Dallas Morning News” tracks
quarterly have less than a two-month supply of houses listed for sale. That is less than a third
of what is considered a normal inventory. According to midyear numbers, It is even less than a
month’s inventory in Colony, Richardson, Bedford and Hurst [2].

The S&P/Case-Shiller Home Price Index is used to measure the health of the U.S. housing
market. It tracks changes in the value of residential real estate, both nationally and in 20
metropolitan regions and is composed of separate indexes which are published on the last
Tuesday of each month, with a two-month lag [22].
• The national home price index, covering nine major census divisions.
• The 10-city composite index
• The 20-city composite index
• 20 individual metro area indexes for each of the cities in the indexes above

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Dallas prices in the Case-Shiller index are now about 14 percent ahead of where they were at
the peak of the housing market before the recession. A limited availability of houses for sale
has increased prices by more than twenty percent in the last year in some neighborhoods in
Dallas-area. Furthermore, raining has held back home construction in North Texas so far in
2015 and has further constrained the housing supply. Many constructions have been delayed
a month because the ground has been too wet to pour slabs and streets [3].

Fig 2: Case- Shiller Home Price Index

2.3 Real Estate Industry in Collin County

Collin County is located north of Dallas County and includes some of the most populous
suburbs of the DFW Metroplex including Plano, Frisco, McKinney, and Allen. Collin County is
approximately 886 square miles and has a population that is expected to top one million in
the next few years.

Recovery in the Texas housing market is occurring considerably from 2012 after the crash of
the housing market. Improvement in economical condition has been increasing demand. The

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high demand and quick selling time have led to an inventory shortage. Decline in inventory
since 2012, started in 2011, has been reducing the supply. This has caused the trend of slow
increasing in prices of houses.

The decrease in average days on market of a single-family home since 2012 supports the
increase in demand. This trend can be observed through charts like supply of home inventory,
average days on market and average price for single homes in Collin County in last 10 years.
These charts have been published in published in Collin county real estate market report.
Following figures are extracted from the Collin county real estate market report [7].

Fig 3: Supply of Single Family Home Inventory in Collin County
As mentioned before, the figure shows continued decrease in supply of single family home
inventory from 2012 after initial drastic decrease from 2011 to 2012 [7].

Fig 4: Average Days on Market of Single Family Homes in Collin County

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The decrease in average days on market of single family home has also observed with respect
to price change, number of bedroom and area in square footage. The strength of our housing
market continues to show steady resilience, trending upward on a progressive incline year to
year. It is consistent with average original list price data [7].

Fig 5: Average Original List Prices of Single Family Homes in Collin County
A strong economy, good schools, low crime rate and a consistent job market is the reason
behind flourishing housing market of Collin County. In 2014, CNN Money Magazine ranked
McKinney 1st place among “Best Places to Live in the United States” [8]. Realtor.com
identified Plano as the country’s 4th hottest zip code in 2013 out of 32,000 others across the
U.S.
Thousands of workers moving to Plano for jobs with Toyota, Liberty Mutual Insurance, FedEx
and others has made housing market of Plano hot never before. Most recently, Plano was
named the 10th best city to find a job in 2015 by WalletHub [8]. We have taken data from
houses located in Plano because Plano’s housing market is booming and it also major reason
behind rise of Collin County housing market. In the next section, we will talk about demand
drivers of housing market.
2.4 Future of house pricing in real estate market
There are two major elements of the housing market, which are residential housing
construction and home sales, or real estate. Moreover, a group of activities such as mortgage
lending, raw materials and housing market-related employment are dependent on new
housing starts and house sales. As to the housing market's future, overall home prices should
at least gradually begin increasing over time [4].

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U.S. housing prices are starting to see modest annual increases. According to University of
California, Berkeley, economist Kenneth Rosen, housing prices may post modest increases of 1
to 2 percent annually through 2020. Adjusted for inflation, housing price increases tend to
follow wage growth; with U.S. wages increasing as the economy gradually improves.
Furthermore, demographic changes in the U.S. population, as children of baby boomers enter
their peak earning years, should help to strengthen housing markets [4].
The housing market took a severe downturn in late 2007 and for a few years after due to
more economic issues. As the economy slowed down starting in mid-2006, mortgage defaults
began increasing and lenders started tightening credit standards, which in turn depressed
housing prices. With unemployment up, fewer people able to afford homes and more people
losing their homes, the housing market crashed. However, as the housing market
reestablishes balance, prices are stabilizing and even starting to rise in many markets [4].
For housing prices to at least rise with inflation, housing markets must be in a balanced state.
A housing market is considered balanced if its housing supply matches the demand for that
housing. During the years before the late-2007 housing market crash, demand for housing
surpassed supply. When the housing market crashed, an oversupply of housing combined
with fewer buyers weakened prices [1]. Nevertheless, many housing markets are now seeing
housing inventories decreasing gradually, with bidding wars even happening in several hot
markets [4].
It is expected that individual markets experience steeper rises than the expected annual
increase of 1 to 2 percent in housing prices. In housing, location is everything, and prime real
estate locations will certainly witness greater price rises. In areas where job growth is strong,
housing prices will react accordingly and start increasing. Finally, real estate markets are
cyclical, with boom-bust cycles as a regular event, meaning within 15 or so years another real
estate boom may happen [4].
Housing price increase of 1 to 2 percent over inflation are a welcome change from late-2007
housing market crash dynamics. It is though unknown whether housing price increases will
again reach their intensity as during the late 2001 to late 2007 real estate boom. What is
obvious and certain is that, in long term, our chasing a house and occupying it because of the
joy of it, is never a losing proposition. For housing market investors, identifying key profitable
markets and then smartly exploiting them usually delivers a winning hand [4].

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3. Demand Drivers
Actual prices and rents are the endogenous determinants of real estate demand of housing.
However, Apart from prices, non-price or exogenous factors have significant influence on
quantity demanded [11]. Real estate analysts give greater importance to the exogenous factors.
Competent forecasts of these factors can be very helpful in assessing real estate market
prospects, evaluating project viability, and identifying real estate development and investment
opportunities. The exogenous drivers of the demand for real estate can be classified into the
following four categories [11]:
• Market Size
• Income/Wealth
• Prices of Substitutes
• Expectations

a) Market size: Major market size variables that drive the demand for housing market are
population and employment. The effect of market size on real estate demand is positive,
that is, for the same price level and larger market size a greater quantity of real estate will
be demanded in terms of either square footage or number of units.
Collin county population has risen from 837,476 in 2012 to 885,241 in 2014 as per
census bureau [12] [13]. Plano population has reached to 267,411 in 2012 to 272,784 in
2014. Moreover, Collin County’s unemployment rate has reduced from 5.9% in 2012 to
4.5% in 2014. The unemployment rate in Plano is 4.3% with job growth of 3.43%. Future
job growth in Plano over the next ten years is predicted to be 42.60%.

b) Income/wealth: It affects directly the demand for residential real estate in the sense that,
keeping prices constant, as income increases more households can afford to buy a house.
Therefore, increases in real income or wealth should be associated with increases in the
number of housing units and the square footage of retail space demanded. The median
household income for Collin County is $81,819, which is 57% higher than that of Texas
($52,130). Median household income for Plano is $95,150 [14]. (Income data is sourced
from census, 2015)

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C) The price of substitutes: It induces shifts in the demand for real estate. For example, for
a given level of single-family housing prices, increases in apartment rents are likely to
induce a shift of the demand curve for single family- housing to the right. Such a shift is
likely to occur because as renting becomes more expensive relative to owning a house
some renters may find home-ownership more attractive.

As per Zillow’s rent versus buy calculator, considering gross costs, equity and investment
potential, it is better to buy than rent if one has plan to live in one’s home more than 1
year and 11 months [15]. Furthermore, Mortgage rate curve has been very slightly
declining from 2013 to present. Mortgage rates are currently low and back in 3s. It has
lowered the cost of a monthly mortgage. It can favor housing market more then rental
market.

D) Expectations: Consumer or firm expectations may induce shifts in demand for the
different types of real estate. For example, expectations of higher prices or rents in the
future may result in increases in the number of housing units demanded. Similarly,
growth expectations may also induce shifts in the demand for housing real estate. For
example, Toyota Motor Company announced that it was moving from its California
headquarters to North Dallas. This induced demand for housing near Plano area.
3. Research Methodology
3.1 Hedonic Methodology

At first glance, aggregate house prices appear easy to measure: Just take the average of all the
house prices in the region. But houses are bought and sold only occasionally. At times other
than the time of sale, the value of a house is determined through an appraisal. But this is only
an educated guess of the sale price of that house if it were sold at the time of the appraisal.
There are different types of methodologies use at the market of houses. For instance: median
sales price methodology, Repeat-sales methodology, and the methodology has been used in
this report is based on hedonic methodology.

Hedonic methodology is an approach to house price index construction that estimates the
typical effects of a house’s attributes on price using regression model. This approach allows
the index to accurately measure the changes in the value of a home over time based on a

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single sale through inference using the typical value associated with the changes in house
attributes over time [16].
3.2 Role of Zillow’s Zestimate

Introduction: The Zestimate home valuation is called Zestimate, computed using a
proprietary formula. It is the starting point in determining a home's value where Zestimate is
calculated from public and user-submitted data taking into account special features, location,
and market conditions. Zestimate is calculated as a value range, which can be interpreted as
highest and lowest estimated values of a home. The range of Zestimate depends on the
magnitude of Zillow’s historical ability to estimate homes. Wider Zestimate value Range
shows less available data and more volatility. On the other hand, a smaller range means
precise values and more availability of data. Generally, Zestimate is calculated at 70%
confidence interval [17].

How is Zestimate calculated? Zestimate considers home characteristics such as square
footage, location, and number of bathrooms [18]. It gives different weights according to their
influence on home sale prices in specific geography over a specific period of time. Attributes
used in the calculation algorithm can be classified as:
• Physical attributes: Location, lot size, square footage, number of bedrooms and no. of
bathrooms and other details.
• Tax assessments: Property tax information, actual property taxes paid, exceptions to
tax assessments and other information provided in the tax assessors' records.
• Prior and current transactions: Actual sale prices over time of the home itself and
comparable recent sales of nearby homes
How accurate is Zestimate? As per available information, in United States Zestimate is
accurate 75% (3 out of 4 times) on an average with highest accuracy of 100% in Arizona and
Alabama. In Texas, Zesitimate’s accuracy drops to 25% (1 out of 4 times). In DFW Region total
number of homes listed on Zillow is close to 2 million (2,096,927) and Zestimate have been
calculated for around 1.9 million homes (1,929,958) with 50% accuracy (2 out of 4 times). For
27% of homes listed in DFW area, Zestimate are 95% accurate.

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3.3 Analysis Steps

1. Following the Hedonic approach, we chose houses from Collin County (Plano area), Texas
from Zillow’s website which were sold in the last 6 months. Only those houses were
considered as a part of this study where actual selling prices was available on Zillow’s
website and were ordered from newest to the oldest.

Fig 8: Zillow’s Real Estate map

2. Houses were filtered based on the variables required for our study viz. “selling price”,
“floor-size-SQFT”, “lot-area-SQFT”, ”price/SQFT”, ”number of rooms”, ”number of baths”,
”age (month)”
3. Houses were tracked and data was collected over a period of 1 month for 40 houses in
Collin County and stored in MS Excel format.
4. Data in the excel format was entered to Business Intelligence Software package “SAS” for
Regression Analysis. s
5. Result screenshots from SAS were captured and analyzed as a part of this report.
6. Hypothesis Testing was done to test whether or not the chosen predictor variables have a
significant impact on the pricing houses.
7. Finally, the Regression Equation was tested with a random sample data of house from
Zillow and results were compared with the Selling Price provided on Zillow.

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4. Regression Analysis
4.1 Data Description

The MS Excel file (ref. Appendix) contains information for 40 houses located in Plano region
picked up from Zillow’s website which were sold in the last 6 months. We will model the
selling prices of these houses using the predictor variables
• Floor_Size_sqft = Floor Size in squarefeet
• Lot_Area_sqft = Lot area in squarefeet
• Pricepersqft = Price of lot per sqft
• No_of_Rooms = Number of bedrooms
• No_of_Baths = Number of bathrooms
• Age_months = Age of the house since it is built (In months)

Fig 9: Price Distribution of chosen houses
4.2 Regression Model Building

The regression equation is set up as:
Selling_Price (House) = β1 + β2 *Floor_Size_sqft + β3* Lot_Area_sqft + β4* Pricepersqft +
β5* No_of_Rooms + β6* No_of_Baths + β6* Age_months

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Using SAS, we performed regression analysis on the data collected. SAS gives us the following
output.

Fig 10. SAS Output
4.3 Model Interpretation
4.3.1 Analysis of Variances (Table 1)
This table performs the Initial Hypothesis Testing as described below to help us understand if our
regression model is effective.
• Ho: If there is no significant effect of independent variables on dependent variable
i.e. all the model coefficients are zero.
o β1= β2= β3=0
• H1: If there is any significant effect of independent variables on dependent variable.
o β1≠ β2≠ β3≠0

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In this case, the p-value associated with the above F-statistic is <.0001 hence, we REJECT our Ho
and conclude that there is significant effect of independent variables on dependent variable.
Therefore, we proceed with the regression analysis.
4.3.2. Overall model fit (Table 2)
R-square: In this table the R-Square value is of prime importance to us. R-Squared is the
proportion of variance in the dependent variable (Selling_Price) that can be explained by the
independent variables (Floor_Size_sqft, Lot_Area_sqft, Pricepersqft, No_of_Rooms,
No_of_Baths and Age_months).
This is an overall measure of the strength of association and does not reflect the extent to
which any particular independent variable is associated with the dependent variable.
4.3.3 Parameter Estimates (Table 3)
This table gives us the values for coefficient estimates that can be used in the regression
equation for predicting the dependent variable from the independent variables. Hence, our
predicting equation is given by:
Selling_Price = -508091 + 119.67390*Floor_Size_sqft + (-3.79773)* Lot_Area_sqft +
3548.00900* Pricepersqft + 18242* No_of_Rooms + 1392.68477* No_of_Baths + 150.594663 *
Age_months

Pr > |t|- This column shows the 2-tailed p-values used in testing the following hypothesis

• Ho: The coefficient of the respective parameters is zero. i.e. βi=0 for all i = 1 to 6
• H1: The coefficient of the respective parameters is significantly different from zero.
i.e. βi≠0 for all i = 1 to 6

Using a confidence level (alpha) of 0.05
• The coefficient for Floor_Size_sqft (119.67390) is significantly different from zero
because its p-value is <0.0001, which is smaller than 0.05.
• The coefficient for Lot_Area_sqft (-3.79773) is not statistically significant at the 0.05
level since the p-value 0.1064 is greater than .05.
• The coefficient for Pricepersqft (3548.00900) is statistically significant because its p-
value <0.0001 is smaller than 0.05.

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• The coefficient for No_of_Rooms (18242) is not statistically significant because its p-
value of 0.0799 is greater than .05.
• The coefficient for No_of_Baths (1392.68477) is not statistically significant because its
p-value of 0.9049 is greater than .05.
• The coefficient for Age_months (150.594663) is statistically significant because its p-
value of 0.0147 is less than .05.
• The intercept is also significantly different from 0 at the 0.05 alpha level.
4.3.4. Predictor Effects of the Model
For each significant coefficient i.e. Floor_Size_sqft , Pricepersqft and Age_months
• The estimate of 119.67390 for Floor_Size_sqft indicates that
o 1 square feet increase in the Floor size will increase the selling price of the house
by approximately $119.
• The estimate of 3548.00900 for Pricepersqft indicates that
o $1 increase in Price of lot increases the selling price of the house by approximately
$3548.
• The estimate of 150.594663 for Age_months indicates that if
o House was 1 month older then the value of the selling price of the house increase
by approximately $150.
4.4 Using the Model to estimate a house value
Let’s take an example and predict the selling price of a randomly selected house from Zillow:

Attribute Value
Address 7017 Brook Forest Cir Plano, Texas - 75024
Floor Size 3324 ft.
Lot Area 7187 Sq. ft.
Price per sqft $ 141/ ft.
No of Rooms 4 Beds
No of Baths 4 Baths

http://www.zillow.com/homes/recently_sold/Plano/TX/65699878_zpid/53915_rid/33.175491
,-96.513348,32.946741,-96.959668_rect/11_zm/?3col=true

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Selling_Price = -508091 + 119.67390*3324 + (-3.79773)* 7187+ 3548.00900* 141 + 18242* 4 +
1392.68477*4 + 150.594663 * 135 = $461,549

By using the specifications of the house as mentioned in the above link in our regression
equation we get the price of the house as $461,549 which is approximately equal to the
actual selling price of the house given on Zillow i.e. $467,500
5. Estimation in the Real world
5.1 Comparison between leading websites

Fig11: Top 10 websites for Real Estate Pricing

Websites
Attributes Listed
Sold
Price
Floor
Size
Lot
Area
Price/
Sq. ft.
No. of
Rooms
No. of
Baths
Built
In

Zillow x x x x x x x
Movoto x x

x x x

Trulia

x

x x x x
Realtor

x x x x x x
RealtyTrac x x x x x x x

Table 1: Comparison of attributes available on different websites

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As per Table 1 most popular real estate website in US in Zillow followed by 36 Million monthly
visitors followed by Trulia with 23 Million monthly visitors. Out of the 5 websites researched for
study, only Zillow and Realtytrac consider all the parameters that we wanted to include for our
study. Realtytrac is not popular in the Real Estate industry, which is why the Zillow was used for
our research [18][19][20][21].
5.2 Accuracy comparison between Zillow and Trulia

Fig 12: Accuracy comparison between Zillow and Trulia

As indicated in the Fig 12, 9 out of 20 times Zillow’s Zestimate has been in the range of (95% -
105%) v/s Trulia is only 5 out 20 times within the range. This proves that Zillow’s Estimate is
closer to the Sold price of house. AS per study, Trulia overestimated the price of the house most
of the times, as their estimates are above 110 % more than the Sold Price of the house. Also,
Trulia does not estimate the price of the house #, rather estimates the price of Zip code, which is
also not very accurate [15][18].

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6. Observations and Conclusions

• Exogenous factors like Market Size, Income/Wealth, Prices of Substitutes, Expectations
are convincing to support boom in housing market of Collin County, specifically in Plano.
This has been confirmed by Collin county market report.
• The final regression equation formed after the analysis was found to be comparable
(98.72% accurate) with a random house price picked from Zillow. According to
Regression Analysis the most significant factors affecting the house price were found to
be floor size, price per sq.ft. and age of house. 97.6% variation in house price is explained
by the variation in the chosen factors.
• Out of the five different websites chosen for research, only two websites Zillow and
RealtyTrac considers all the factors for finding house price.
• When Zillow was compared with Trulia, Zillow came out to be more accurate than Trulia
in estimating the selling price of the house.

7. Future Scope

• This study can be extended to include various other parameters (macroeconomic and
industry related) that are not considered right now, but can be included to find out their
impact.
• Comparison studies can be done comparing the Regression formula for Plano v/s nearby
areas? This will help understand the differences which factors are more important within
nearby areas.
• Similar studies can be extended to compare prices of Dallas with other metropolitans for
e.g. New York, to check which factors are more important as the city changes.

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8. References
• http://homeguides.sfgate.com/causes-housing-prices-rise-united-states-56413.html [1]
• http://www.dallasnews.com/business/residential-real-estate/20150723-scorching-
dallas-area-home-prices-mean-tough-summer-market-for-buyers.ece [2]
• http://www.dallasnews.com/business/residential-real-estate/20150428-dallas-area-
home-prices-continue-to-soar-at-twice-national-average.ece [3]
• http://homeguides.sfgate.com/housing-market-dynamics-future-housing-prices-52296.html
[4]
• http://homeguides.sfgate.com/housing-market-dynamics-future-housing-prices-
52296.html [5]
• http://www.ebizmba.com/articles/real-estate-websites [6]
• http://www.homesourcedallas.com/reports/collin-county-real-estate-market-report [7]
• http://thefejerangroup.com/tag/collin-county [8]
• http://www.econlib.org/library/Enc/Supply.html [9]
• http://www.davidhoma.com/196570-piy-ch19-01.pdf_275935.pdf [10]
• http://isites.harvard.edu/fs/docs/icb.topic1143374.files/Rena__Chap%202.pdf [11]
• http://www.txcip.org/tac/census/profile.php?FIPS=48085 [12]
• http://www.county.org/about-texas-counties/countdata/Documents/towns.html [13]
• http://www.realtor.com/local/Plano_TX/lifestyle [14]
• http://www.zillow.com/rent-vs-buy-calculator [15]
• https://philadelphiafed.org/research-and-data/publications/research-rap/2014/house-
price-indexes.pdf [16]
• http://www.zillow.com/zestimate/#faq-6 [17]
• http://www.trulia.com/ [18]
• http://www.movoto.com/ [19]
• http://www.realtor.com/ [20]
• http://www.realtytrac.com/ [21]
• http://us.spindices.com/indices/real-estate/sp-case-shiller-20-city-composite-home-
price-index/ [22]

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