Search results

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.

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.

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

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.

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.

In this chapter, we aim to analyze the housing market development in Czechia, in particular the development of housing prices over the last 25 years. We quantify and discuss three distinct periods of excessive growth of regional Czech housing prices, identified through the formation of large positive GAPs – (1) before the entrance of Czechia to the European Union (EU), (2) at the onset of the Global Financial Crisis GFC, (3) in 2021. In all these periods, we identify significant differences among regions. We find that GAPs above 15% may be considered an indication of unsustainable long-term housing price growth that will be followed by a correction.

We then employ fixed effect panel data model to determine the drivers of flat and house prices in 14 Czech regions. Our results show that wage growth, migration and crime rate are significant factors affecting the prices of both flats and houses. Nevertheless, the impact of GDP per capita and job market indicators differs between flats and houses. Moreover, we find that higher migration into the region increases the difference between the prices of houses and flats, while increasing GDP per capita growth and crime rate mitigate this difference significantly.

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

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

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.

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.

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.

Downward movements in house prices can exacerbate bank crises if mark‐to‐market methods of asset valuation are used by lenders to assess their current balance sheet exposure. There is an imperative to find methods of house price index calculation that reflect equilibrium prices rather than temporary undershoots. The purpose of this paper is to propose a new methodology in order to evaluate whether market house prices are different from their fundamental asset prices.

This paper proposes a method for house asset valuation that incorporates expected house price appreciation as an endogenous variable. This avoids the necessity to make conjectures about expected future house price appreciation when applying Poterba's user‐cost method of house asset valuation. The methodological extension to Poterba's user‐cost method of house asset valuation endogenises expected house price appreciation as the no‐arbitrage expected price appreciation consistent with the term structure of real interest rates. A benchmark equilibrium house valuation can be calculated because the term structure of real forward interest rates is observable in financial markets. This enables market house prices to be compared with the benchmark equilibrium valuation in order to determine if house prices are overvalued or undervalued.

The paper presents the results of a worked example to illustrate how this approach could be applied in practice.

There are a number of issues associated with the measurement of user cost which we do not address here and which the authors hope will provide fruitful avenues for future research. There are also issues regarding the impact of tax frameworks on the returns to housing, particularly the taxation of mortgage interest and imputed income. More work also needs to be done in comparing the performance of the extended Poterba model against alternative approaches, such as those that use expected inflation and/or long‐run average house price appreciation, or the real interest rate spread to proxy for expected capital appreciation, and how these different approaches compare in different institutional and socio‐economic contexts.

The authors' results underscore the rationale for mortgage banks to use marking to model instead of marking to market, and this in turn should reduce unnecessary macroeconomic instability when the market prices of houses undershoot fundamental value.

The paper shows how the term structure of real forward interest rates, observable in financial markets, can be used to extend the Poterba model.

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.

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.

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.

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.

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.

We examine the aggregate price trend of the Greek housing market from a behavioral perspective.

We construct a behavioral real estate sentiment index, based on relevant real estate search terms from Google Trends and websites, and examine its association with real estate price distributions and trends. By employing EGARCH(1,1) on the New Apartments Index data from the Bank of Greece, we capture real estate price volatility and asymmetric effects resulting from changes in the real estate search index. Enhancing robustness, macroeconomic variables are added to the mean equation. Additionally, a run test assesses the efficiency of the Greek housing market.

The results show a significant relationship between the Greek housing market and our real estate sentiment index; an increase (decrease) in search activity, indicating a growing interest in the real estate market, is strongly linked to potential increases (decreases) in real estate prices. These results remain robust across various estimation procedures and control variables. These findings underscore the influential role of real estate sentiment on the Greek housing market and highlight the importance of considering behavioral factors when analyzing and predicting trends in the housing market.

To investigate the behavioral effect on the Greek housing market, we construct our behavioral pattern indexes using Google search-based sentiment data from Google Trends. Additionally, we incorporate the Google Trend index as an explanatory variable in the EGARCH mean equation to evaluate the influence of online search behavior on the dynamics and prices of the Greek housing market.

The purpose of this paper is to develop an index for measuring urban house price affordability that integrates spatial considerations and to explore the drivers of housing affordability using the post-least absolute shrinkage and selection operator (LASSO) approach and the ordinary least squares method of regression analysis.

The study is based on time-series data collected from 2005 to 2021 for 256 prefectural-level city districts in China. The new urban spatial house-to-price ratio introduced in this study adds the consideration of commuting costs due to spatial endowment compared to the traditional house-to-price ratio. And compared with the use of ordinary economic modelling methods, this study adopts the post-LASSO variable selection approach combined with the k-fold cross-test model to identify the most important drivers of housing affordability, thus better solving the problems of multicollinearity and overfitting.

Urban macroeconomics environment and government regulations have varying degrees of influence on housing affordability in cities. Among them, gross domestic product is the most important influence.

The paper provides important implications for policymakers, real estate professionals and researchers. For example, policymakers will be able to design policies that target the most influential factors of housing affordability in their region.

This study introduces a new urban spatial house price-to-income ratio, and it examines how macroeconomic indicators, government regulation, real estate market supply and urban infrastructure level have a significant impact on housing affordability. The problem of having too many variables in the decision-making process is minimized through the post-LASSO methodology, which varies the parameters of the model to allow for the ranking of the importance of the variables. As a result, this approach allows policymakers and stakeholders in the real estate market more flexibility in determining policy interventions. In addition, through the k-fold cross-validation methodology, the study ensures a high degree of accuracy and credibility when using drivers to predict housing affordability.

The purpose of this study is to examine the effects of the Odd-Even Road Rationing Policy (RRP) on housing prices in Jakarta, Indonesia. It aims to evaluate the net effect of the RRP on housing prices.

The study uses the monocentric model and employs the difference-in-differences (DD) method. Annual neighborhood-level housing price data is analyzed to assess the impact of the RRP on housing prices. Additionally, propensity score matching is used to address potential biases resulting from non-random policy assignments.

The results demonstrate that houses located within the RRP-restricted area experience a decrease in price that is relative to those in the control group. The findings indicate a decrease in housing prices ranging from 7.59% to 14.7% within the RRP-restricted area. This suggests that the positive impacts resulting from the RRP have not fully compensated for the restricted accessibility experienced by individuals who have limited behavioral changes. The study also confirms the significance of commuting costs in individuals' location decisions, aligning with predictions from urban economics models.

This study contributes to the literature by providing insights into the effects of a RRP on housing prices. It expands understanding beyond the immediate effects on traffic conditions and air pollution, which previous studies have primarily focused on. Furthermore, to the best of the authors’ knowledge, this research will be the first conducted to identify the impacts of RRP on housing prices in Indonesia.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.