The spatial structure of Istanbul housing market

The Spatial Structure of Istanbul Housing Market

Berna Keskin (Ph.D Candidate)

Town and Regional Department

The University Of Sheffield

Outline of the Presentation

1.

Aim & Objectives

2.

Turkish Property Market and Istanbul

3.

Data, Model and Results

Introduction: Aim & Objectives

Aim:

The purpose of this research is to understand the determinants of the housing prices.

This presentation reports on the first stage of a PhD thesis.

The main aim of the thesis is to develop a model of house prices that captures neighbourhood-level price difference in Istanbul.

Objectives:

1. To identify the structure of the owner occupied housing market

2. To display the factors that influence house prices?

3. To examine relationship between locations and housing prices

4. To understand the behavioural influences on spatial structure of the housing

– Population :10,033,478.

— Istanbul population/Turkey : 14.78 % in 2000 (TUIK,2006).

— 2,550,000 households and 3,391,752 housing units

— Ownership: 68 % in Turkey: 58 % in Istanbul.

– the problems:

— high increase rate in population,

— the gap in the incomes

— lack of enough amounts of residential plots.

— unrestored historical residential areas at the city center

— 54 % of the city is informal settlements

— 30 % informal settlement spread out the water dam and forest area

— after the 1999 Marmara Earthquake the construction of gated communities in the forest area and water dam area

— land rent and speculation.

– the housing price gap among the neighbourhoods : spatial inequalities , social,

economic inequalities. the housing prices per m² : 400 $ to 4000$ (Hurriyet Emlak 2006).

– the physical and socio-economic structures of the neighborhoods have sharp

differences even they are next to each other.

Housing Prices Change in Istanbul

After the Marmara Earthquake in 1999 and the economic crisis in 2000 and 2001, the property market ceased in Turkey especially in Istanbul.

During 1997-2005 period, increase in house sales prices in Istanbul materialized as 67%, compared to housing price increases of 189% in Romania and 72% in Slovakia.

Prices of apartment type projects that are

scheduled to be delivered in 2008 increased by 25% with respect to 2006 delivery units.

The property sales increased 25 % during 2003-2005 period. Source: www. tuik.gov.tr.

Method

–

This paper reports on the first stage of a larger research project . There

are 2 steps, the first step reports the results of the basic hedonic model .

–

A hedonic model is employed to estimate house prices within Istanbul but

largely ignores neighbourhood differences.

–

to understand the housing price determinants,

— The physical structure of the housing unit

— Socio- economic structure of inhabitants

— Behavioural structure of the inhabitants

— The physical characteristics of the housing unit

Data

1. Survey: from “Istanbul Strategical Plan”

— 799 neighbourhoods in Istanbul (IBB, 2006) ; categorized in nine groups

according to the land prices and land densities.

— In nine groups, 100 buildings were selected randomly and out of 900 point, in

300 buildings the residential and life quality survey were held.

— Sample size 3864.

2. Secondary Data:

— The advertisements in the websites for the month of November in 2006 and

in April 2007.

— The two main property agencies –Turyap and Remax

— Sample size 2179.

–

Property Characteristics: from property agencies

–

Socio-economic, Behavioural and Built Environment Characteristics :

municipality survey

Model and Results

Predictors: (Constant), continent, log_playsat, low_str, logarithm of tha age, log_trvtimejob, log_eqr1, garden, log_hhsize, site, log_neigsat, log_schoolsat, log of sqm, log_livper_ist, logarithm of the avarage income, log_healsat a. ANOVAb 93.769 15 6.251 156.122 .000a 60.141 1502 .040 153.910 1517 Regression Residual Total Model 1 Sum of

Squares df Mean Square F Sig.

Predictors: (Constant), continent, log_playsat, low_str, logarithm of tha age, log_ trvtimejob, log_eqr1, garden, log_hhsize, site, log_neigsat, log_schoolsat, log of sqm, log_livper_ist, logarithm of the avarage income, log_healsat

a.

Dependent Variable: Log of the housing price b. Coefficientsa 1.693 .155 10.948 .000 1.150 .030 .704 38.259 .000 .285 .054 .112 5.298 .000 .174 .029 .119 5.940 .000 .086 .016 .096 5.378 .000 .055 .011 .094 5.185 .000 -.120 .019 -.108 -6.251 .000 .025 .011 .037 2.194 .028 .180 .081 .040 2.215 .027 -.014 .013 -.019 -1.093 .275 .115 .059 .060 1.932 .054 -.070 .074 -.018 -.947 .344 -.001 .024 -.001 -.038 .970 -.088 .049 -.051 -1.801 .072 .001 .012 .002 .123 .902 (Constant) log of sqm log_livper_ist logarithm of the avarage income site

Model and Results

P= f ( Fa, I, Lp, Eq, S, A, Ls, N)

Fa: Floor

Area

I:

income of the household

Lp:

Living Period in Istanbul

Eq: Earthquake

Damage

S: Site

A: Age

Ls: Low

Storey

Further Studies

This paper reports on the first stage of a larger research project. At this stage, much of this research is still at the developmental stage.

This research project seeks to build on the existing studies of the Istanbul market. Specifically the research aims to develop a model of house prices that captures neighbourhood-level price differences.

The research employs a multi-level modelling framework as the main analytical tool. The results of the multi-level model are examined in several ways.

First, the results are compared to those generated by two different forms of the standard hedonic model. The first hedonic model estimates house prices within Istanbul but largely ignores neighbourhood differences. The second model

includes neighbourhood dummy variables, as a proxy for submarkets, within the model. The comparative analysis compares the estimated coefficients,