US Housing Market: What does affect house prices and how the prices differ between

metropolitan areas

A n d r e a s A v g o u s t i

I D : 9 8 2 3 9 0

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Table of Contents

1. Introduction………………………………………………………………2

2. Background………………………………………………………………4

3. Descriptive Statistics…………………………………………………….9

4. Empirical Analysis………………………………………………………16

5. Conclusion……………………………………………………………….23

6. Bibliography………………………………………………………………24

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1) Introduction

Recent events in the US housing market highlighted the importance of house prices and

how they differ from time to time and place to place. The most recent event that is worth

mentioning is the housing bubble that recently busted causing serious problems in the

US economy. Through the years house prices were increasing in a rapid way which

caused them to peak in mid-2006. Late in 2006 and by the beginning of 2007 the prices

drastically decreased which has led the housing market into collapsing. Late in 2008 the

Case-Shiller index recorded the largest price drop in history.

The affordable housing loan and having low interest rates for a long time are the main

factors that helped the creation of the housing bubble. We should also have in mind

that the belief of American Citizens that house ownership is a good investment is

another major factor that caused this bubble. Americans borrowed money in order to

buy a house of their own and later on, the increase of the interest rates and the

downturn in the general economic activity led into unaffordable housing loans. People

could no longer easily buy houses which led into a decrease in demand, an increase of

supply and all that led into the fall of house prices, causing the bubble to burst.

More recent indexes and studies show that house prices are getting back up in a rapid

pace, which forces some economists into thinking that there might be another bubble on

the horizon. Due to that, further analysis of the house prices and the housing market

itself will be a great advantage. Reaching a greater understanding of the housing

market itself, can help us resolve or even foresee problems that may arise affecting the

housing market.

There is a difference in house prices between metropolitan areas resulting in some

questions that needs to be answered. House prices vary based on specific structural

attributes. It is essential to understand the structure of determinants of house prices in

order to make reliable estimates on their effects. Developing a greater understanding of

what really affects house prices may result into a greater understanding of the housing

market itself. Although there are several factors that affect house prices many of them

are highly correlated resulting into a more difficult to explain analysis.

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The determinants of housing prices render the housing market more interesting. Why do

certain characteristics increase or decrease the price of a house? Some of those are

obvious but some of them are not. The most interesting variable is the location of the

house. House prices differ between metropolitan areas. Extracting the mean value of

the price of a house with the same characteristics but located in a different metropolitan

area will help us compare the prices between metropolitan areas and compare

investor’s behavior.

As we already know individuals are interested in maximizing their utility. Investing in a

home is a very important decision every person has to take during his life span.

Another interesting subject related to the housing market is the decision of a household

or an individual to buy a house and how they respond to a given price structure based

on their characteristics. The importance of this particular analysis is that almost every

family or individual will go through that decision once or even more times in some

occasions. Understanding what urges individuals to buy a house will be a great

advantage. It can help us to control the housing market better by changing the way the

market offers the houses. We can build less of a kind that people are not very attracted

to and also we can “modify” the supply of houses into what people need the most.

This is the exact purpose of this analysis. Focusing on the factors that affect house

prices and measure their effect. By that we can further understand the variation

between house prices and refine the major attributes that have an impact on them with

a slightly greater interest on the location of the house.

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2. Background

According to Charles Kindleberger a bubble is “a sharp rise in price of an asset or a

range of assets in a continuous process, with the initial rise generating expectations of

further rises and attracting new buyers. The rise is usually followed by a reversal of

expectations and a sharp decline in price often resulting in financial crisis”. In theory, in

any efficient market there should be no bubbles because the price is always determined

by the laws of supply and demand which leads into the question, is the housing market

efficient?

The US housing bubble was not created in recent years but goes back for several more,

around 1990. The late 1990 was the beginning of a sharp shit in the U.S. housing

market.

The above graph (Margaret Hwang Smith and Gary Smith (2009), Bubble, Bubble,

Where is the Bubble?) shows the radical growth of house prices from the first quarter of

1990 to 2009. We can see the prices grow until the mid of 2006 where the bubble

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“busted”. After the end of 2006 and the beginning of 2007 the prices began to drop in a

fast pace. Case-Shiller index recorded a 8.7 percent increase in house prices from

1990 to 1997. In the mid 2006 during the price peak the Case-Shiller index recorded a

massive 132 percent increase compared to the prices of 1997.

This development was a result of several actions made by the US Government or even

buyers themselves. Several Government regulations, case in point, the Taxpayer Relief

Act of 1997 established a $500,000 married/$250,000 single exclusion of capital gains

on the sale of a home, available once every two years. This not only encouraged

investors/buyers into buying expensive houses but also into buying more than one

(Howard Chernick,Adam Langley and Andrew Reschovsky, Predicting the Impact of the

U.S. Housing Crisis and “Great Recession” on Central City Revenues). Another major

cause was the low mortgage interest rates.

Foreign investors were seeking low risk investments. They turned to mortgage-backed

securities resulting into a great descent of the mortgage rates. As you can see in the

above graph, mortgage rates began to descent at a great rate after 1984 coming all the

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way down to 6 percent at 2005 (Jeff Holt, A Summary of the Primary Causes of the

Housing Bubble and the Resulting Credit Crisis). Buyers/Investors thought that house

prices will continue to go up since home prices had not descended since the Great

Depression. Due to that credit agencies rated the housing market with the highest mark

possible which led into the deception that the market was doing good. Buyers continued

to buy houses with the assumption that they could later sell the house for a higher price

and make profit. All of that in combination with several other reasons led into the burst

of the bubble in 2006.

Another interesting development is the price range of houses after the burst of the

bubble.

In the graph above you can see the Case-Shiller 20-city Home price index. (The

S&P/Case-Shiller 20-City Composite Home Price Index seeks to measures the value of

residential real estate in 20 major U.S. metropolitan areas: Atlanta, Boston, Charlotte,

Chicago, Cleveland, Dallas, Denver, Detroit, Las Vegas, Los Angeles, Miami,

Minneapolis, New York, Phoenix, Portland, San Diego, San Francisco, Seattle, Tampa

and Washington, D.C.).

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The Case-Shiller index shows rapid increase in house prices after 2010. The growth

rates are similar or even higher to the growth rates that we saw before the bubble

busted, which has lead several economists into thinking that the United Stated might be

in the middle of another housing bubble.

We should develop a greater understanding of the housing market in order to foresee or

even prevent future bubbles from emerging. To accomplish that we must first

understand how house prices are developed. Several studies identified how the house

characteristics can affect the price of a house.

(R. K. Wilkinson,1973, House prices and measurement of

Externalities)

Structural characteristics affect housing prices in different ways, for example an

increase in the construction date, which makes the house older, will decrease the price

of a house. This philosophy applies to several other structural variables that combined

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create the entity called a house. Some variables are highly correlated, for example if

the size of a house increases then the price will increase as well, but also an increase in

the Garage space will increase the overall house size as well. Based on the analysis

showed above, the increase of the Garage space will decrease the price of a house

which contradicts the previous statement. As we can see the house size variable and

the garage size variable are negatively related to each other which makes the analysis

a bit more difficult to explain in full.

There is much more to be done in developing a greater understanding of the effects of

the internal and external aspects of a house on their market value.

In my analysis i will focus on the main factors that affect house prices. I will be using

data across different metropolitan areas for 2013 (IPUMS database). The purpose of

this analysis is to determine the different structural characteristics that affect house

prices and understanding the price difference in between metropolitan areas.

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3. Descriptive Statistics

Descriptive statistics will help us understand better how the housing market works. We

extracted some structural components of houses in the United States and we will measure their

effect on house prices. The structural components we will be using are the number of

bedrooms, the number of rooms and the age of structure.

Our data were extracted by the American Community Survey*. There are 544766 unique

households.

Theoretically, we expect those factors to affect the prices in different ways. Through descriptive

statistics we see how many houses have each characteristic and the mean price of each

category.

We will analyze various tables and charts that will help us concentrate our analysis on the

proper factors.

No of Bedrooms

Category No. of Houses Mean House Price

0-1 7852(1%) 182545

2 80949(14%) 189262

3 272549 (50%) 238849

4 or more 183416 (33%) 422582

Total: 544766

No of Rooms

Category No. of Houses Mean House Price

1 and 2 2000(1%) 201206

3 6872(1%) 177444

4 35984(7%) 181424

5 97403(18%) 196105

6 120479(22%) 231434

7 98744(18%) 279274

8 or more 183284(34%) 418199

Total: 544766

Age

Category No. of Houses Mean(%)

0-2 years 4301 (1%) 406611

3-5 years 11829(2%) 364352

6-10 years 20949(4%) 327872

11-15 years 45661(8%) 326257

16-20 years 80589(14%) 302445

21-25 years 79686(14%) 276840

26 years or more 301751(55%) 280389

Total: 544766

10

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0-1 2 3 4 or more

As you can see, the mean price of houses changes based on the characteristics it possess as

we expected. While the number of rooms, bedrooms and the age of structure changes we see

changes in the mean price as well. We will further analyze those results later in this paper.

Price Difference Based on No. of Bedrooms

The number of bedrooms in a house clearly affects its price. Through the above graph we can

come to the conclusion that a house with more bedrooms will have a higher price if we keep

every other variable constant. Although it’s not clear by how much each additional bedrooms

affect the price, we will come to a conclusion later on.

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0

50000

100000

150000

200000

250000

300000

350000

400000

450000

1 and 2 3 4 5 6 7 8 or more

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

0-2 years 3-5 years 6-10 years 11-15

years16-20

years21-25

years26 years

or more

Price Difference Based on No. of Rooms

As with our previous graph, the numbers of rooms affect the price of a house as well. We can

see a difference in the mean price of houses with fewer rooms in comparison with houses with

additional rooms, which help us conclude to that, houses with more bedrooms will have a higher

price than houses with fewer bedrooms due to the utility it provides and to that it is of greater

size.

Price Variation based on Age of Structure

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0100000200000300000400000500000600000700000800000900000

1000000

90000

95000

100000

105000

110000

115000

120000

125000

The age of structure is another variable that appears to affect prices as well. As we expected,

the price decreases while the structure is of older age. That might be the case due to

depreciation.

Top 10 Metropolitan Areas based on mean price

Bottom 10 Metropolitan Areas based on mean price

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190000

192000

194000

196000

198000

200000

202000

204000

206000

208000

The above graphs demonstrate how the prices differ according to where the structure is located.

Clearly the location of a house has a major effect on its price. There is a huge difference in the

prices of houses across all metropolitan areas. We can see the top 10 most expensive

metropolitan areas as well as the bottom 10 least expensive metropolitan areas. This is very

interesting we will examine the reason why this is happening later on. We will examine several

factors that may explain this variation of prices among metropolitan areas. The prices of a

house with the exact same structural characteristics such as age, number of bedrooms and

rooms differ by a lot based on where its located.

Median

(200000)

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Non-Structural Components

As I previously stated, houses in different metropolitan areas have a huge difference in their

prices. This may be caused due to several characteristics each area possesses. The

Student/Teacher ratio, Criminality, People per square mile, the number of infant deaths appear

to have an effect on how the prices of houses are formed. Student/Teacher ratio is a measure of

the education level of an area. A higher ratio should increase the price of a house. The number

of people per square mile also has a positive effect on house prices. The more people live in an

area the higher the price of houses will be. The rate of crimes that take place in an area is also

a factor that may affect house prices. The more crimes take place in an area, the lower the price

will be.

Hopefully the above variables can explain the difference of prices between different

metropolitan areas. The characteristics of a metropolitan areas may have an effect on each

house independently or as an area as well.

Student/Teacher Ratio

Category No. of Houses Mean Price

0-15 269281 281999

15-20 201565 227662

20+ 95169 445230 Note: Student/Teacher ratio is a measure of education

People per square mile

Category No. of Houses Mean Price

0-100 89469 232717

100-200 186631 266522

200-300 88379 334534

300-400 36568 256488

400-500 32156 277836

500-600 10664 247874

600-700 23864 266325

700+ 77035 414506

Criminality

Category No. of

Houses Mean Price

0-200000 177386 301959

200000-300000 86296 227837

300000-400000 122607 250545

400000+ 158477 349684

Infant Deaths (Rate per 1000 births)

Category No. of Houses Mean Price

0-5 93635 456995

5-10 417009 263207

10-20 31138 189268 Note: Infant deaths is a measure of the level of

health care.

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We will run different regression models comparing the fixed effects of metro areas to a

regression having these variables along with our structural variables and see if those variables

can be identified as the ones that affect the prices.

The above graph demonstrates the distribution of house prices. As we have

seen above several factors affect the price of a house. Later in this paper we will run

some regression models to double check our results and see exactly how those factors

affect the prices.

Total No. of Houses

Mean Value of House prices

Std. Dev.

544766 292530 371079

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4. Empirical Analysis

Metropolitan Area FE No Yes No

(1) (2) (3)

Bedrooms *Category Var: 3 bedrooms

1 Bedroom -0.251 (0.01)** 0.235 (0.008)** -0.237 (0.009)**

2 Bedrooms -0.189 (0.003)** -0.188 (0.003)** -0.169 (0.003)**

4 or more bedrooms 0.317 (0.002)** 0.204 (0.002)** 0.238 (0.002)** Rooms *Category Var: 6 Rooms

1-4 Rooms -0.207 (0.005)** -0.262 (0.004)** -0.259 (0.004)**

5 Rooms -0.131 (0.003)** -0.132 (0.003)** -0.14 (0.003)**

7 Rooms 0.122 (0.003)** 0.127 (0.002)** 0.137 (0.003)**

8 or more Rooms 0.364 (0.003)** 0.382 (0.002)** 0.410 (0.003)** Age *Category Var: 16-20 years

0-10 years 0.137 (0.004)** 0.185 (0.004)** 0.150 (0.004)**

11-15 years 0.068 (0.004)** 0.083 (0.004)** 0.067 (0.004)**

21-25 years -0.03 (0.04)** -0.109 (0.003)** -0.085 (0.003)**

25 Years or more -0.07 (0.003)** -0.212 (0.002)** -0.117 (0.003)** Student/Teacher Ratio *Category Var: 20+ S/T

1-15 S/T - - 0.537 (0.002)**

16-20 S/T - - 0.503 (0.003)** People per square mile *Category Var: 0-300

300-400 - - 0.075 (0.004)**

400-500 - - 0.046 (0.004)**

500-700 - - 0.07 (0.007)**

700+ - - 0.276 (0.03)** Criminality *Category Var: 300k-400k

0-200k - - 0.210 (0.002)**

200k-300k - - -0.036 (0.003)**

400k or more - - 0.092 (0.003)** Infant Deaths *Category Var: 5-10

0-5 - - 0.4152(0.003)**

10-20 - - -0.208(0.03)**

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We used 3 different models to extract information on how our variables affect the price

of a house. The models we used are:

1) Dependent Variable: log(valueh) Independent Variables: bedrooms, rooms, age

of structure

2) Dependent Variable: log(valueh) Independent Variables: bedrooms, rooms, age

of structure, absorbing the effects of metropolitan areas as fixed effects.

3) Dependent Variable: log(valueh) Independent Variables: bedrooms, rooms, age

of structure, student/teacher ratio, criminality, infant deaths, people per square

mile.

You used the logarithm of the value to eliminate modeling errors due to

heteroscedasticity. Also we estimated a separate model using the metropolitan area as

fixed effects to check how the location of a structure affects the coefficients of our

variables.

Regression 1

As we can see from the table above, the prices of houses alter depending on how many

bedrooms there are in a house. Houses with one bedroom appear to have 25% lower

price in comparison to the base variable which is houses with 3 bedrooms. Houses with

two bedrooms have 19% lower price that the base variable and houses with 4

bedrooms have higher price at the rate of 31%. Having in mind those results we can

safely conclude that the more bedrooms a house have, the higher the price will be.

Another variable we tested is the number of rooms that exist in a house. Houses with 1

to 4 rooms tend to have 20% lower price that houses with 6 rooms. Houses with 5

rooms tend to have 13% lower price than the base variable and houses with 7 or 8

rooms appear to have 12% and 36% higher price than the base variable respectively.

So the number of rooms have a positive effect on the house prices meaning that, the

more rooms of house has, the higher its price will be.

The last variable we are going to examine in our first model is the age of structure. A

house that is at maximum 10 years old has a 13% increase in its price in comparison to

a house that was built 15 or 20 years ago. Houses with an age between 11 and 15

years have 7% increased priced compared to the base variable. Houses with age

between 21 and 25 years have 3% lower price compared to the base variable and

houses with age more than 25 years have 7% decrease in their price compared to the

base variable.

In conclusion houses that are older have lower price compared to houses that were built

recently.

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Regression 2

In this model we controlled our variables based on the location of the house

(metropolitan area). We used the location as fixed effects.

The explanation of their coefficients is the same as the last model but with different

values.

Houses with one bedroom appear to have 23% lower price in comparison to the base

variable which is houses with 3 bedrooms. Houses with two bedrooms have 19% lower

price that the base variable and houses with 4 bedrooms have higher price at the rate of

20%.

Another variable we tested is the number of rooms that exist in a house. Houses with 1

to 4 rooms tend to have 26% lower price that houses with 6 rooms. Houses with 5

rooms tend to have 13% lower price than the base variable and houses with 7 or 8

rooms appear to have 13% and 38% higher price than the base variable respectively.

The last variable we are going to examine in our first model is the age of structure. A

house that is at maximum 10 years old has a 18% increase in its price in comparison to

a house that was built 15 or 20 years ago. Houses with an age between 11 and 15

years have 8% increased priced compared to the base variable. Houses with age

between 21 and 25 years have 10% lower price compared to the base variable and

houses with age more than 25 years have 21% decrease in their price compared to the

base variable.

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Regression 3

Our third model includes several characteristics for each metropolitan area. We are

going to check how those characteristics affect the house prices and how the variable

coefficients changed in comparison to our other models.

First we will analyze the coefficients of the variables that we had in the other two models

as well.

Houses with one bedroom appear to have 23% lower price in comparison to the base

variable which is houses with 3 bedrooms. Houses with two bedrooms have 16% lower

price that the base variable and houses with 4 bedrooms have higher price at the rate of

23%.

Houses with 1 to 4 rooms tend to have 26% lower price that houses with 6 rooms.

Houses with 5 rooms tend to have 14% lower price than the base variable and houses

with 7 or 8 rooms appear to have 13% and 41% higher price than the base variable

respectively.

A house that is at maximum 10 years old has a 15% increase in its price in comparison

to a house that was built 15 or 20 years ago. Houses with an age between 11 and 15

years have 6% increased priced compared to the base variable. Houses with age

between 21 and 25 years have 8% lower price compared to the base variable and

houses with age more than 25 years have 11% decrease in their price compared to the

base variable.

Now we are about to analyze some variables that contain information about the location

of the house.

Student/Teacher ratio as we previously stated reflects the level of education in an area.

Compared to our base variable, as we can see having a low student teacher ratio

decreases the price of a house, in our case the base variable is 20 or more students per

teacher so, anything below that will decrease its price.

Another variable we used is the number of people per square mile. We used the 0-300

interval as our base variable. As you can see while the number increases, the prices

increase as well.

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Now that we have analyzed each regression separately, we should compare them to

extract the effect of location on those coefficients.

Our first regression as well as the second one uses the exact same variables but the

difference is that out second regression uses the metropolitan area the house is located

at as fixed effects.

Let’s compare the variables one by one and check how they differ. As we can see the

number of bedrooms is almost the same between the two regressions with the

exception of the houses with 4 or more bedrooms. For that category we can see that

there is more than 1% difference between the two regressions. That might be the case

because houses with 4 or more bedrooms are located in more expensive metropolitan

areas, which causes the coefficient to be slightly lower in our regression that uses the

location fixed effects.

The numbers of rooms’ coefficients are also almost identical across the two regressions

with the category of 1 to 4 rooms being the exception. The category of 1 to 4 rooms in

our first regression is 0.50% lower than the regression which controls for location. This

is happening because most of the houses with 1 to 4 rooms are located in less

expensive metropolitan areas which cause the coefficient to be slightly lower.

Having in mind the above results we can conclude to that houses that are “bigger” of

sorts, tend to be in the most expensive metropolitan areas but is this the reason why

there is a difference between similar houses that are located in a different area? Or

maybe other variables affect the price as well? As we have previously discusses we will

analyze more variables to see if this is the case.

Now, an interesting result is the difference between the coefficients of the age variable.

As we can see, houses with an age of 0-15 years tend to have a similar change in price

across our two regressions. Houses with 21-25 years although seem to differ. It appears

that older structures tend to be located in our least expensive metropolitan areas. This

might be the case because of recent changes in the job market. More developed

metropolitan areas may offer a higher amount of job opening so people tend to build

there in recent years in order to eliminate the long trips that are needed to get to work.

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Now let’s take a look at our third regression. Our third regression uses the same

variables as our first two but with the difference that we have added several other

variables that show certain characteristics that each metropolitan area has. Those

variables are: the Student/Teacher ratio which is a measure of education found in an

area, the number of People per square mile, the number of crimes committed in an

area, and the number of infant deaths which is a measure of health care.

This regression answers various questions that we may previously have had. We can

see that the coefficients found in regressions one and three are different. Some not so

much but other vary greatly. This is because; in our third regression we used variables

that explain the difference of prices across metropolitan areas.

To back that claim up we can compare the second regression to our third. The second

regression uses the metropolitan area variable as fixed effects while our third regression

does not but it contains variables that may explain that difference. As we can see the

coefficients of those two regressions are almost identical. They have minor differences

because of errors which are caused by missing variables. Now if we had all the

variables that define a metropolitan area, the coefficients of our regressions would be

exactly the same. If we could manage to include all location variables that affect the

selling price of a house there would be no need to use a fixed effects model because

the results would be identical.

Through our previous regressions and analysis we came to the conclusion that, the

structural characteristics of a house affect greatly the price of a house, each one in a

different way. Although the interesting part was the location variable. First we assumed

that the location by itself could alter the price of a house, but later with regression

analysis we came to the conclusion that it’s not the location by itself that can alter the

price but the variables that construct a metropolitan area. Each of those variables have

a different effect on the price of a house so it is clear that each metropolitan area with a

different set of variables can have a different mean price for a house with the same

structural characteristics.

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5. Conclusion

The recent housing market bubble and generally the latest economic crisis have shown

the importance of the housing market. The housing market is an important bone of the

economy and should be definitely treated as one. An important factor that led to the

economic crisis that hit the United States is the collapse of the Housing market at the

fall of 2007.

The prices of houses are directly linked to mortgages and could lead to extreme

problems to the banking sector, if the prices change drastically. A decrease of the price

of a house which is mortgaged could lead to a decrease in the mortgage forcing the

bank to record losses in their books.

The housing market is not only essential to the economy as a whole but to each person

separately as well. No one can argue that everybody’s dream is to get a house of their

own and start a family and that is the main reason why everyone should be informed of

how the prices are developed.

Coming back to our own findings, we have analyzed how the prices are affected by their

own structural characteristics and how the location does its part. That’s only the

Microeconomic aspect of things. House prices are greatly affected by other

Macroeconomic factors as well. For example inflation, construction costs, the per capita

GDP etc.

Consider the results, rapid changes of prices can be predicted and eliminated through

on the point analysis in order to prevent future bubbles or any sort of problem affecting

the housing market.

In conclusion, the housing market should be closely controlled by the government or

any trustworthy third-party in order to assure its well-being and not to be turned into a

mean of making money. Without regulation the housing market will always be

vulnerable and subject to exploitation.

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Bibliography

1) Pashardes P., Savva Ch., 2009. Factors Affecting House Prices in Cyprus: 1988-

2008.

2) Sean Holly ,M.Hashem-Pesaran, Takashi Yamagata, 2010. A spatio-temporal

model of house prices in the USA

3) Michaelides M., 2010. The effect of local ties, wages, and housing costs on

migration decisions

4) Margaret Hwang Smith and Gary Smith, 2006. Bubble, Bubble, Where is the

housing bubble?

5) Jesse M. Abraham and Patric H. Hendershott, 1992. Patterns and Determinants

of Metropolitan House Prices, 1977 to 1991

6) R. K. Wilkinson, 1973. House Prices and the Measurement of Externalities

Web Sources

1) The Economist: http://www.economist.com/blogs/graphicdetail/2014/02/us-

house-prices

Data Sources

1) IPUMS-USA, University of Minnesota, www.ipums.org

Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2010.