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Article
Publication date: 1 June 2012

Hassan Gholipour Fereidouni

In recent years, housing prices and rents have recorded impressive growth in Iran. Several observers believe that real estate agents have had a significant effect on this…

1286

Abstract

Purpose

In recent years, housing prices and rents have recorded impressive growth in Iran. Several observers believe that real estate agents have had a significant effect on this phenomenon. However, some do not agree with this viewpoint and argue that the role of real estate agents is not that much and housing prices and rents are affected by macroeconomic factors. The purpose of this paper is to investigate whether real estate agents can influence housing prices and rents across provinces of Iran.

Design/methodology/approach

Applying panel data technique, this paper uses observations from 28 provinces of Iran covering 2000 and 2003 to examine the role of real estate agents on housing prices and rents.

Findings

The empirical results indicate that the increased number of real estate agents and their activities positively significantly stimulate housing prices and rents.

Research limitations/implications

To the author's knowledge, most studies in this area cover the US and European real estate markets. Since findings for developed countries might not be directly transferable to emerging market economies such as Iran, more work is necessary to obtain a clearer picture of the role of real estate agents on housing prices and rents in emerging economies.

Originality/value

Although there has been a series of cross‐sectional studies published in this area, few empirical works have examined the effects of real estate agents on housing prices and rents by applying panel data set. The paper begins to fill this gap by analyzing a data sample of 28 provinces of Iran covering 2000 and 2003.

Details

International Journal of Housing Markets and Analysis, vol. 5 no. 2
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 1 August 2006

Ming‐Long Lee and R. Kelley Pace

The purpose of this paper is to provide additional evidence that housing prices significantly impact aggregate refinancing and thus directly influence mortgage termination.

1478

Abstract

Purpose

The purpose of this paper is to provide additional evidence that housing prices significantly impact aggregate refinancing and thus directly influence mortgage termination.

Design/methodology/approach

Regression analysis is applied to examine refinancing activity in US cities.

Findings

The evidence shows that positive appreciation in housing prices provides the borrower with positive incentives to refinance in response to the associated increased borrowing capacity when mortgage rates have declined. On the other hand, depreciation in housing prices may depress refinancing.

Research limitations/implications

Housing price movements, not only collateral constraints on refinancing but also the disincentive to engage in cash‐out refinancing caused by depreciation as well as the incentive for cash‐out refinancing brought by appreciation, should be included in modeling total termination risks of mortgage‐backed securities.

Originality/value

In contrast to previous studies, this paper provides empirical support for both the incentive and the disincentive to engage in cash‐out refinancing produced by housing price changes, in addition to support for the traditional collateral constraint effect of housing prices on refinancing.

Details

Property Management, vol. 24 no. 4
Type: Research Article
ISSN: 0263-7472

Keywords

Article
Publication date: 16 November 2022

Ahmet Gökçe Akpolat

This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index…

Abstract

Purpose

This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.

Design/methodology/approach

This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.

Findings

The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.

Originality/value

This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 31 October 2022

Seyedeh Mehrangar Hosseini, Behnaz Bahadori and Shahram Charkhan

The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to…

Abstract

Purpose

The purpose of this study is to identify the situation of spatial inequality in the residential system of Tehran city in terms of housing prices in the year 2021 and to examine its changes over time (1991–2021).

Design/methodology/approach

In terms of purpose, this study is applied research and has used a descriptive-analytical method. The statistical population of this research is the residential units in Tehran city 2021. The average per square meter of a residential unit in the level of city neighborhoods was entered in the geographical information system (GIS) in 2021. Moran’s spatial autocorrelation method, map cluster analysis (hot and cold spots) and Kriging interpolation have been used for spatial analysis of points. Then, the change in spatial inequality in the residential system of Tehran city has been studied and measured based on the price per square meter of a residential unit for 30 years in the 22 districts of Tehran by using statistical clustering based on distance with standard deviation.

Findings

The result of spatial autocorrelation analysis with a score of 0.873872 and a p-value equal to 0.000000 indicates a cluster distribution of housing prices throughout the city. The results of hot spots show that the highest concentration of hot spots (the highest price) is in the northern part of the city, and the highest concentration of cold spots (the lowest price) is in the southern part of Tehran city. Calculating the area and estimating the quantitative values of data-free points by the use of the Kriging interpolation method indicates that 9.95% of Tehran’s area has a price of less than US$800, 17.68% of it has a price of US$800 to US$1,200, 25.40% has the price of US$1,200 to US$1,600, 17.61% has the price of US$1,600 to US$2,000, 9.54% has the price of US$2,000 to US$2,200, 6.69% has the price of US$2,200 to US$2,600, 5.38% has the price of US$2,600 to US$2,800, 4.59% has the price of US$2,800 to US$3,200 and finally, the 3.16% has a price more than US$3,200. The highest price concentration (above US$3,200) is in five neighborhoods (Zafaranieh, Mahmoudieh, Tajrish, Bagh-Ferdows and Hesar Bou-Ali). The findings from the study of changes in housing prices in the period (1991–2021) indicate that the southern part of Tehran has grown slightly compared to the average range, and the western part of Tehran, which includes the 21st and 22nd regions with much more growth than the average price.

Originality/value

There is massive inequality in housing prices in different areas and neighborhoods of Tehran city in 2021. In the period under study, spatial inequality in the residential system of Tehran intensified. The considerable increase in housing prices in the housing market of Tehran has made this sector a commodity, intensifying the inequality between owners and non-owners. This increase in housing price inequality has caused an increase in the informal living for the population of the southern part. This population is experiencing a living situation that contrasts with the urban plans and policies.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 29 May 2009

Onur Özsoy and Hasan Şahin

The purpose of this paper is to analyze empirically major factors that affect housing prices in Istanbul, Turkey using the classification and regression tree (CART) approach.

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Abstract

Purpose

The purpose of this paper is to analyze empirically major factors that affect housing prices in Istanbul, Turkey using the classification and regression tree (CART) approach.

Design/methodology/approach

The data set was collected from various internet pages of real estate agencies during June 2007. The CART approach was then applied to derive main results and to make implications with regard to the housing market in Istanbul, Turkey.

Findings

The CART results indicate that sizes, elavators, existance of security, existance of central heating units and existance of view are the most important variables crucially affecting housing prices in Istanbul. The average price of houses in Istanbul was found to be 373,372.36 New Turkish Liras. The average size of a house was 138.37 m2. The average age of houses is 15.07 years old with the average number of rooms being 3.11. The average number of baths is 1.43 and average number of toilets is 1.22. Only 5 percent of homes have storage space, 45 percent of homes have parking space, 64 percent of homes are heated with furnace, whereas only 29 percent of homes are used central heating system. Among the 31 variables employed in this study, it was concluded size, elavator, security, central heating unit and view are the most important factors that have impact on housing prices in housing market in Istanbul.

Practical implications

Future research and analysis of housing market in Istanbul and in Turkey can benefit from the method used in this study and findings derived from this research to come up with more general model(s) to include more houses in a wide range of regions in Turkey to analyze the determinants of housing prices in Turkey in general.

Originality/value

Examining housing prices using the CART model is relatively new in the field of housing economics. Additionally, this study is the first to use the CART model to analyze housing market in Istanbul and in Turkey and derive valuable housing policies to be used by the authorities.

Details

International Journal of Housing Markets and Analysis, vol. 2 no. 2
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 16 September 2022

Xin Janet Ge, Vince Mangioni, Song Shi and Shanaka Herath

This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value.

Abstract

Purpose

This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value.

Design/methodology/approach

Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model.

Findings

Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results.

Research limitations/implications

It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions.

Practical implications

While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security.

Originality/value

The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 5 October 2022

Fredrick Otieno Okuta, Titus Kivaa, Raphael Kieti and James Ouma Okaka

This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL…

Abstract

Purpose

This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index.

Design/methodology/approach

This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited.

Findings

The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers.

Practical implications

The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations.

Originality/value

The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 22 September 2022

Rafiq Ahmed, Hubert Visas and Jabbar Ul-haq

This study aims to explore the impact of oil prices on housing prices using Pakistani annual data from 1973 to 2021.

Abstract

Purpose

This study aims to explore the impact of oil prices on housing prices using Pakistani annual data from 1973 to 2021.

Design/methodology/approach

The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests were used for unit-root testing, whereas the johansen-juselius test was used for cointegration. For the short-run, the error correction model is used and the robustness of the model is checked using the dynamic ordinary least squares (DOLS) and fully modified OLS (FMOLS). The cumulative sum (CUSUM) and CUSUM of Squares tests were used to check the stability of the model, while parameter instability was confirmed by the Chow breakpoint test. Finally, the impulse response function was used for causality.

Findings

According to the findings, rising oil prices, among other things, have an impact on housing prices. Inflation is the single most important factor affecting not only the housing sector but also the entire economy. Lending and exchange rates have a significant impact on housing prices as well. The FMOLS and DOLS results suggest that the OLS results are robust. According to the variance decomposition model, housing prices and oil prices are bidirectionally related. The Government of Pakistan must develop a housing policy on a regular basis to develop the country’s urban housing supply and demand.

Practical implications

It is suggested that in Pakistan, the rising oil prices is a problem for the housing prices as well as many other sectors. The government needs to explore alternative ways of energy generation rather than the heavy reliance on imported oil.

Originality/value

Pakistan has been experiencing rising oil prices and housing prices with the rapid urbanisation and rural–urban migration. The contribution to the literature is that neither attempt (as to the best of the authors’ knowledge) has been made to check the impact of rising oil prices on housing sector development in Pakistan.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Article
Publication date: 22 September 2022

Na Li and Rita Yi Man Li

This paper aims to provide a comprehensive bibliometric study of housing prices according to the articles collected by the Web of Science (WOS).

Abstract

Purpose

This paper aims to provide a comprehensive bibliometric study of housing prices according to the articles collected by the Web of Science (WOS).

Design/methodology/approach

This paper studies 4,125 research papers on housing prices in the core collection database of WOS. Using VOSviewer, this paper makes a bibliometric and visual analysis of the housing prices research from 1960 to 2020 and probes into the housing prices research from five aspects: time, international cooperation, institutions author cooperation and research focuses.

Findings

Keywords such as influencing factors of housing prices, analysis of supply and demand, policy and housing prices and regional cities appear frequently, which indicates the main direction of housing price research literature. Recent common keywords include regression analysis and house price forecast. Countries, like the USA started early in the study of housing prices, and the means and methods in the field of housing price research are mature, leading the forefront of housing price research. Compared with the USA and other Western developed countries, the housing price research in developing countries needs to use innovative research methods and put more effort on sustainability. Research shows that housing price is closely related to economy, and keyword cluster analysis shows that gross domestic product, interest rate, currency and other keywords related to economy are of high-frequency.

Research limitations/implications

This paper only uses articles from one database (WOS), which does not represent all research papers published worldwide. Some studies have been published for a long time, and the reference value to the research focuses and future research might be limited. There are many kinds of journals included in the study with different publishing frequencies, time ranges and numbers of papers. These may have some influence on the research results.

Originality/value

The main theoretical contribution of this paper is to supplement the current academic research on housing prices. This paper reveals the key points of housing prices research and possible research problems that need attention. We can know from the future research direction and practice which can offer insights for future innovative direction.

Details

International Journal of Housing Markets and Analysis, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1753-8270

Keywords

Book part
Publication date: 25 May 2021

M. Ozan Yildirim

Introduction: Financial development has a direct impact on the housing market by facilitating access to credit. The increase in housing loans resulting from the relaxation…

Abstract

Introduction: Financial development has a direct impact on the housing market by facilitating access to credit. The increase in housing loans resulting from the relaxation of the credit constraint causes an increase in housing demand and house prices. Purpose: This study aims to examine the relationship between financial development and house prices in Turkey, using the variables: the domestic credit to the private sector and total housing and consumer credits. Methodology: To determine any long-run relationship between financial development and house prices, the autoregressive distributed lag methods are used, covering the selected variables such as real GDP, inflation, mortgage interest rate, and stock price from 2010Q1 to 2020Q2. Findings: The study’s findings show that both variables representing financial development have a statistically significant and substantial positive effect on house prices. Besides, the selected macroeconomic variables have the theoretically expected impact on house prices.

Details

Contemporary Issues in Social Science
Type: Book
ISBN: 978-1-80043-931-3

Keywords

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