Non-western immigrants as a determinant of residential property prices in Rotterdam, The Netherlands

8/2/2019 Non-western immigrants as a determinant of residential property prices in Rotterdam, The Netherlands

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Non-western immigrants as a determinant of residential property prices in

Rotterdam, The Netherlands

Arjan van der Laan

University of Ljubljana, Faculty of Economics


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TABLE OF CONTENTS

Introduction ............................................................................................................................................. 3

Assumptions & definitions ...................................................................................................................... 4

Data ......................................................................................................................................................... 5Descriptive statistics ................................................................................................................................ 6

Analysis .................................................................................................................................................... 6

Conclusion ............................................................................................................................................... 7

Bibliography ............................................................................................................................................. 9

APPENDIX I ............................................................................................................................................ 10

Data assumptions .............................................................................................................................. 10

APPENDIX II ........................................................................................................................................... 15


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INTRODUCTION

Residential property demand is affected by different factors, which combined determine the

price of a particular house, apartment or other dwelling place. In the real estate business it issometimes joked there are three key determinants of the price of a house are: Location,

location and location (Hamel). This, together with the characteristics of the house have been

shown to be indeed the two biggest factors explaining the variability in prices of residential

property (Visser, Van Dam, & Hooimeijer, 1999) (Grether & Mieszkowski, 1974). In my own

hometown Rotterdam it seemed that one of the determinants of house prices was the amount

of immigrants in the neighborhood, particularly non-western immigrants. Though in history

ethnic groups were often grouped together involuntarily, Anno 2012 in The Netherlands

ethnic concentration is also increasing (Jones-Correa, 2001) (Vervoort, 2011). Based on their

own research, the PBL Netherlands Environmental Assessment Agency (2006) concludes that

the amount of non-western immigrants have a strong effect on the prices of houses in The

Netherlands (Visser & van Dam, 2006).

This research will investigate if the presence of non-western immigrants (N i), size in m2 (Si),

location (Li) and age (Ai) are indeed related to the price (Vi) of a residential house in

Rotterdam. In a very simplified manner we use so called sub-determinants to account for

location and property characteristics.

The hypotheses concerning the results of the regression analysis are the following:

The price of presence of non-western immigrants in the neighborhood has an effect

on residential housing prices, aside of other usual other determinants size, age and

location.1

a. The coefficient NONWEST is expected to be negative and statisticallysignificant (p


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ASSUMPTIONS & DEFINITIONS

Dependent variable

The dependent variable in this research is the price in Euro of a residential property in the city

of Rotterdam, The Netherlands within its borders.

Because Rotterdam is a densely populated city, the majority of observations will comprise

apartments rather than freestanding houses.

Independent variables

Variable: percentage of non-western immigrants in the four position postcode area.

The percentages of non-western immigrants is measured as a proportion of the total

population within a four position postcode area. An immigrant is a person of whom at least

one parent is foreign-born. Non-western immigrants are those from Turkey, Africa, Latin

America, or Asia (except Indonesia and Japan).

There are several characteristics of non-western immigrants that play a role in their place in

society, among which cultural (difference with ethnic Dutch), physical (neighborhood),

economical (average wage, unemployment) and social (education level) (Nieuwenhuis &

Hooimeijer, 2011). All of these we combine. Therefore, after this investigation, we can or

cannot conclude that the mere fact of being an immigrant has an effect on the price of

property around you. Statements about the effect of the presence of immigrants in this

research report have to be read with great caution and with an invitation to further research

that combines abovementioned characteristics.

Variable: Size of the apartment or house in square meter

Apart from the amount of non-western immigrants, several other factors have been identified

in earlier research as key determinants. If all factors would be perfectly distinguished we

would see a sort of tree model in which there are several layers of determinants. Every layer

has a most important determinant, which is build up by several other sub-determinants of

which each has a different strength of impact on the determinant. In order to greatly simplify

this model, we will have to use the key sub-determinants that have the biggest impact of that

group of sub-determinants.

The first and most obvious determinant of housing prices in a particular area is the

combination of physical characteristics of the house. Because there are many sub-

determinants combined in this, we take the one that is proven the most important one, namelythe size of the house in square meters (Grether & Mieszkowski, 1974).

Variable: Property age

As a second important characteristic of the house, I take into account the age of the house. As

stated in the introduction this also has a (minor) relationship with property value. The age is

calculated as follows: 2012 minus the year in which the property is build.

Variable: summed amount of distance in meters from closest known necessary and

entertainment facilities

The amount of non-western immigrants is just one aspect of many that can be summarized asthe location of the house. Apart from the demographics of the neighborhood, a strong


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determinant is the (combined) availability of fundamental necessities such as shopping and

entertainment facilities.

To account for this, we use the summed amount of meters of distance that the property is

away from a number of those facilities, as will be shown in the chapter on data.

1 key determinant of housing prices that falls under location is the availability of jobs in the

area (Kok, Monkkonen, & Quigley, 2011). By only investigating property in the city itself, we

also control for the statistically supported assumption that job access is higher in the city than

around the city (Port of Rotterdam, 2010).

DATA

The sample will be consist of 118 elements.

Variable: price of a residential property in Euro in the city of Rotterdam, The Netherlands

(PRICE)

The elements of the research, the residential properties and their respective characteristics, are

obtained from a website on which every broker is allowed to place houses, without limit. This

website is http://www.jaap.nl. The search criteria used are City: Rotterdam, with a price range

from 0 to unlimited. In order to get a random sample we obtain the data sorted from the

newest to the oldest, taking the first 120 houses.

Variable: percentage of non-western immigrants in the four position postcode area

(NONWEST)

The proportion of non-western immigrants is takes from openly available statistical

information supplied by the Gemeente Rotterdam (Municipality Rotterdam) and are retrievedfrom http://rotterdam.buurtmonitor.nl/. The proportions are divided by 4 position postcode

area. These postcode areas have an average of 6859 inhabitants (lowest 0, highest 27236)

Variable: Size of the apartment or house in square meter (M2)

Variable: Age of the property in years (AGE)

These are obtained from the house information at www.jaap.nl.

Variable: summed amount of distance in meters from closest known necessary and

entertainment facilities (FACILITIES)

These are obtained from the house information at http://www.detelefoongids.nl which has allpublic addresses of every legal entity. They comprise the following facilities and their

respective distance from the house in question:

Closest doctor

Closest dentist

Closest elementary school

Closest high school

Closest bar

Closest fitnesscentreClosest video library


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Closest childcare

Closest supermarket

Closest public library

Closest gas station

Closest train station

DESCRIPTIVE STATISTICS

n min. max. x SEx s s2

PRICE 120 59000 275000 134810.42 4476.931 49042.327 2405149838

M2HOUSE 120 43 130 67.84 1.774 19.432 377.597

FACILITIES 120 3929 12339 7149.85 186.996 2048.443 4196118.683

NONWEST 120 10 79 39.24 1.629 17.840 318.252

AGE 120 2 112 54.88 2.571 28.165 793.264

The average housing price of the sample is around 135,000 with a lowest value of 59,000

and a highest value of 275,000. The distribution is slightly positively skewed (0.517) but still

significant and has a standard deviation SD of roughly 50,000 as is visible in the table above.

9 houses had a price below 75,000.- while 15 houses had a price of 195,000.- or higher.

The average size of the house in the sample is 67.84 square meters (SD=19). The average

total distance from the 12 facilities is 7149.85 meter, which means each house is on average

596 meters away from the nearest listed facilities. Because the entire sample comes from one

city alone, this number could be very different when taking it on a national level.

In every neighborhood of the sample, at least 5% of the inhabitants is a non-western

immigrant, topping at a maximum of 79% in the Afrikaanderwijk (Official name which,

freely translated, means neighborhood of Africans. The name was given long before mass

immigration started) area.Figure 1Figure 6 (Appendix II) shows the dispersion of immigrants

in the city of Rotterdam.

Houses in the sample are on average around 55 years old (SD=28) and there are no houses

older than 112 years in the sample.

ANALYSIS

For clarity we show the hypothesis:

Using the forward regression method, three of the four predictors have been kept as they were

statistically significant. The variable FACILITIES has been dropped with (in the last step) a

significance of .492.

With the variables M2HOUSE, NONWEST and AGE (added in this order) in the model,

around 69% of the variability in the dataset can be explained (.000 sig.) with a very smalldifference between R squared and the adjusted form. By far the biggest part of the explanation


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comes from M2HOUSE with a standardized beta weight of .686 of the coefficient, showing

an R-squared value of .534 in the first step of the forward regression.

AGE is the weakest predictor as is also visible in the scatter plot against PRICE in appendix I.

Model R R2 Adj. R2 SEE Change StatisticsR

2Change F change Sig. F change

1 .731 .534 .530 33608.918 .534 135.384 .000

2 .819 .671 .665 28364.261 .137 48.672 .000

3 .833 .694 .686 27475.397 .023 8.693 .004

Unstd. coefficients Std. bi t Sig. 95% CI for b

b sb1 beta Lower UpperConstant 55116.085 13603.507 4.052 .000 28172.624 82059.545

M2HOUSE 1730.653 132.764 .686 13.036 .000 1467.697 1993.609

NONWEST -978.742 141.865 -.356 -6.899 .000 -1259.723 -697.761

AGE -271.202 91.985 -.156 -2.948 .004 -453.389 -89.014

FACILITIES .492

The estimated multiple regression equation is:

(2)

The presence of non-western immigrants shows to be stronger related to price than ageing is,

but less than the size of the house. For every added square meter, the price of a house in

Rotterdam would increase between around 1467.70 and 1993.61 (95% confidence).

A 1 percent increase in the amount of non-western immigrants in the 4 digit postcode area

appears to have a negative effect of on average 979, with a confidence interval of 697.76 to1259.72. Ageing has the smallest and least significant relationship, where every extra year of

age would go along with a 271 drop in house price.

CONCLUSION

There is enough evidence to conclude that at least one (three) of the predictors is useful for

predicting PRICE, rejecting H0.

We accept Ha(a) stating that the presence of non-western immigrants has a negativerelationship with housing prices. Although postcode area is sometimes a big area making it


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hard to prove practical significance in all cases, the relationship is a clear one. A 25 percent

growth in amount of non-western immigrants in the area showed a decrease in price of almost

25,000. (using the coefficient).

As an obvious and often proven determinant, size of the house has a strong and clear

relationship with price in the sample. Age however is also linear but has a very big variance

making it less valuable for an individual estimation. Still it is significant (also practically) and

therefore an important sub-determinant of residential housing price.

Why the distance from facilities is not significant is not clear. We expect it is because al

observations were done in the same city, where nothing is actually far away. Subjectively

looking at the average distance from a facility of only 614 meters, this would seem viable.

However further research is needed to quantify the significance on study of a wider area,

preferably the whole country at once.

Further research is needed to apply this on a national level, in more detail (characteristics) andadding subjective factors to measure location also in a way people perceive it, rather than just

how far away it is from a list of facilities.


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BIBLIOGRAPHY

Grether, D., & Mieszkowski, P. (1974). Determinants of Real Estate Values.Journal of urban

economics , 137.

Hamel, G. (n.d.). What Determines Property Value?Retrieved 02 11, 2012, from eHow Money:http://www.ehow.com/how-does_4895257_what-determines-property-value.html

Jones-Correa, M. (2001). The Origins and Diffusion of Racial Restrictive Covenants. Political Science

Quarterly, Winter, 20002001 (Vol. 115, No. 4 ), 541568.

Kok, N., Monkkonen, P., & Quigley, J. M. (2011, Februari). Economic Geography, Jobs, and

Regulations: The Value of Land and Housing . Utrecht, Hong Kong, Berkeley.

Nieuwenhuis, J., & Hooimeijer, P. (2011). Neighbourhood effects on school achievement mediated by

problematic behaviour and parenting. Utrecht: Faculty of Geosciences, Utrecht University.

Port of Rotterdam. (2010). Cijfers werkgelegenheid (Figures job availability). Retrieved 02 11, 2012,

from Website of Port of Rotterdam: http://www.portofrotterdam.com/nl/Over-de-

haven/arbeidsmarkt-haven/Pages/cijfers-werkgelegenheid.aspx

Visser, P., & van Dam, F. (2006). Kwaliteit woonomgeving bepaalt groot deel van huizenprijs.

Ruimtelijk Planbureau. Den Haag: NAi Uitgevers, Rotterdam.

Visser, P., Van Dam, F., & Hooimeijer, P. (1999). RESIDENTIAL ENVIRONMENT AND SPATIAL

VARIATION IN HOUSE PRICES IN THE NETHERLANDS. Tijdschrift voor economische en sociale

geografie , 348-360.


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APPENDIX I

DATA ASSUMPTIONS

Normality

The multivariate distribution is normal after regression:

Univariate distribution is fairly normal, except for M2HOUSE which is just over the critical

(p=.01) value of .581. Due to the importance of the variable and the small error we keep it.

N Skewness Kurtosis

Statistic Statistic Std.

Error

Statistic Std.

Error

M2HOUSE 120 ,588 ,221 -,128 ,438

PRICE 120 ,517 ,221 -,401 ,438

FACILITIES 120 ,372 ,221 -,691 ,438NONWEST 120 ,340 ,221 -,543 ,438

AGE 120 -,113 ,221 -,950 ,438


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Outliers

Studentized residual

With a sample size of 120, observations with a Studentized residual of t0.25=1.98 (where

df=(n-1)-p-1) or higher as an absolute value are outliers. All of the outliers are checked andexamined.

#

SRE

PRICE

NONWEST

AGE

M2HOUSE

FACILITIES

Action

Comment

1 2,10416 224000 42 74 100 5412 Retained Residential property andthus retained.

2 2,64584 198000 42 73 76 6612 Retained Although this house is a

National Monument, it is

purely residential.

3 2,42994 215000 29 61 80 9107 Retained Although a house on an

excellent location, its

normal residential property.

4 2,10233 159500 36 79 60 6252 Retained Residential property and

thus retained.

5 2,02331 275000 14 8 105 11920 Retained Residential property and

thus retained.6 -2,93865 69000 13 25 65 8848 Retained Part of a flat mostly but not

only aimed at elderly people.

7 -2,13636 185000 13 34 130 7595 Retained Residential property and

thus retained.

Leverage

Using a critical value of 2p/n=0.084 for the leverage values, one observation stands out:

#

Leverage

PRICE

NONWEST

AGE

M2HOUSE

FACILITIES

Action

Comment

1 .11237 242000 79 8 125 6185 Retained Residential property.

Although highly influential, the property is residential and no errors were made.


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Homoscedasticity

Regression standardized residual plots for each of the independent variables against PRICE

show an approximate homoscedastic relationship.

Y=Regression Standardized ResidualX= Regression Standardized Predicted residual

Figure 1: Residual plot M2HOUSE

Figure 2: Residual plot NONWEST

Figure 3: Residual plot AGE


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Linearity

All the eventually regressed variables show a linear relationship. In terms of linearity is the

relationship NONWEST-PRICE the least linear, but still enough to assume linearity. In terms

of strength is AGE the weakest of the three, though still effective.

Residual analysis

7 outliers exist and are outside of the 2 acceptable range for the standardized residual

analysis (1.98 for studentized residual). I conclude the error term is normally distributed.

Figure 4 a/b/c: bivariate linearity

Figure 5: Residual analysis


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Multicollinearity

There is some correlation between the independent variables, but very little and not enough to

prove multicollinearity.2

Also, there is no VIF value higher then 3, all are between 1 and 1.1.

Therefore we reject the hypothesis that they would be correlated.

Model Correlations Collinearity Statistics

Zero-order Partial Part Tolerance VIF

1(Constant)

M2HOUSE ,731 ,731 ,731 1,000 1,000

2

(Constant)

M2HOUSE ,731 ,782 ,719 ,999 1,001

NONWEST -,393 -,542 -,370 ,999 1,001

3

(Constant)M2HOUSE ,731 ,771 ,669 ,953 1,049

NONWEST -,393 -,539 -,354 ,990 1,010

AGE -,339 -,264 -,151 ,945 1,058

2Note that FACILITIES is not in the eventual regression model.

VIF valuesIndependent variable

M2HOUSE NONWEST AGE

Dependent

variable

M2HOUSE - 1,010 1,010NONWEST 1,049 - 1,049

AGE 1,001 1,001 -


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APPENDIX II

Figure 6: percentage of non western immigrants per neighborhood, 2011

Non-western immigrants as a determinant of residential property prices in Rotterdam, The Netherlands

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