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The main purpose of this study is to detect homogeneous housing market areas among 196 districts of 5 major cities of Turkey in terms of house sale price indices. The second purpose is to forecast these 196 house sale price indices.

In this paper, the authors use the monthly house sale price indices of 196 districts of 5 major cities of Turkey. The authors propose an autoregressive (AR) model-based fuzzy clustering approach to detect homogeneous housing market areas and to forecast house price indices.

The AR model-based fuzzy clustering approach detects three numbers of homogenous property market areas among 196 districts of 5 major cities of Turkey where house sale price moves together (or with similar house sales dynamic). This approach also provides better forecasting results compared to standard AR models by higher data efficiency and lower model validation and maintenance effort.

In this study, the authors could not use any district-based socioeconomic and consumption behavioral indicators and any discrete geographical and property characteristics because of the data limitation.

The finding of this study would help property investors for establishing more effective property management strategies by taking different geographical location conditions into account.

From the government side, knowing future rises, falls and turning points of property prices in different locations can allow the government to monitor the property price changes and control the speculation activities that cause a dramatic change in the market.

There is no previous research paper focusing on neighborhood-based clusters and forecasting house sale price indices in Turkey. At this point, it is the first academic study.

This paper aims to answer how effective the loan-to-value (LTV) regulation has been since 2011 for conventional and Islamic (participation) banks in Turkey in terms of curbing mortgage loan growth and delinquency[1].

The authors first use unit root tests and tests of difference in loan and property price data in pre-LTV and post-LTV period. Second, the authors follow Chow test and ordinary least squares regression analyses to test for a structural break when sensitivity of mortgage loan and delinquency growth changes to property price changes considered.

The authors find that two periods are statistically different, while the significance level is lower for Islamic banks. Moreover, loan growth has become less responsive to property price increases; delinquency sensitivity to property price changes has significantly increased in the post-LTV period for conventional banks, while this is not the case for Islamic (participation) banks.

This paper not only increases empirical evidence regarding the effectiveness of LTV ratio policy but also fills the gap in the literature by providing a comparison between conventional banks and Islamic (participation) banks.

It is important to forecast index series to identify future rises, falls, and turning points in the property market. From the point of this necessity and importance, the main purpose of this paper is to forecast the future trends in Dubai housing market.

This paper uses the monthly time series of Reidin.com Dubai Residential Property Price Index (DRPPI) data. In order to forecast the future trends in Dubai housing market, Box‐Jenkins autoregressive integrated moving average (ARIMA) forecasting method is utilized.

The results of the ARIMA modeling clearly indicate that average monthly percentage increase in the Reidin.com DRPPI will be 0.23 percent during the period January 2011‐December 2011. That is a 2.44 percent increase in the index for the same period.

Reidin.com residential property price index is a crucial tool to measure Dubai's real estate market. Based on the current index values or past trend, real estate investors (i.e. developers and constructors) decide to start new projects. Attempts have also been made in the past to forecast index series to identify future rises, falls, and turning points in the property market. The results of this paper would also help government and property investors for creating more effective property management strategies in Dubai.

There is no previous study analyzing the future trends in Dubai housing market. At this point, the paper is the first academic study that identifies this relationship.

The purpose of this paper is to review the housing sector of Turkey and present the housing development strategies developed by government enterprises for the urban poor in Turkey as successful examples.

The methodology of the paper is descriptive. First of all, the literature on housing finance systems and sources of housing finance are stated. Then, the paper reviews housing finance systems applied in Turkey in the past to solve housing problems. Later, it describes current housing strategy to solve housing problems of low- and middle-income groups and also presents this strategy as a successful model to other countries. Moreover, mortgage law and the current situation of the Turkish housing sector are discussed within the study.

As a result of economic normalization achieved after 2002, mortgage loans extended by commercial banks have increased in Turkey. Besides, governmental institutions, such as Housing Development Administration of Turkey (HDAT) and Istanbul Public Housing Corporation (KIPTAS), apply very extensive projects to allow low- and middle-income groups to have their dwellings. In 2007, the Turkish Parliament enacted mortgage law and defined rules and actors of the mortgage sector. However, as a consequence of economic deterioration in the world economy, mortgage loan receivables-backed securities could not be issued to public yet. Public issuance of mortgage loan receivables-backed securities in the future are expected to direct more long-term funds to the housing sector and also to provide an additional investment instrument for the individual and institutional investors.

The housing production and finance models developed by the HDAT and KIPTAS can be good models for the solution of housing problems of urban poor in other countries.