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The purpose of this study is to investigate the determinants of returns on housing in Iran by analyzing capital appraisals, rents, and total returns.

This study uses data from 28 provinces of Iran over the period of 2000‐2007. Using generalized method of moments (GMM), the determinants of returns on housing in Iran are investigated.

The empirical results indicate that in particular changes in inflation and population and to a lesser extent changes in unemployment and gross domestic product (GDP) are the important determinants of returns on housing.

The findings imply that real estate investors in Iran can obtain higher returns from their housing investment if they invest in provinces that have positive changes in population, GDP and inflation and negative changes in unemployment rate.

Most studies in this area cover US and European real estate markets (particularly office market). Since findings for developed countries might not be directly transferable to emerging market economies such as Iran, therefore, more work is necessary to obtain a clearer picture of real estate markets in emerging market economies.

The purpose of this study, the nonlinear relationship between the real estate market and the stock market was investigated in Iran. For this intent, the monthly data from 2012:4 to 2022:5 is used.

In this study, the quantile-on-quantile estimation method is used, which is a combination of the nonparametric estimation methods and the quantile regression.

The research results show that, in the low quantiles, the effect of stock market return on the housing market return is negative or zero. In fact, in this situation, the increasing returns in the stock market will shift part of the financial resources of the economy to the market and create stagnation or even negative returns in the housing market. This situation is seen more strongly in some other quantiles, including the 0.25 and 0.75 quantiles; in contrast, the effect of high quantiles of stock market returns is positive on the housing market.

It seems that the demand in the housing market increase in a situation where the returns of the stock market are growing, and the market is in a bullish condition, and this causes an increase in the price and returns in this market. In addition, the results show that the effect of stock market returns on capital market returns is asymmetric and nonlinear.

The diffusion of innovations in student housing, a commercial real estate subsector, is a critical concern to developers. Aside from how innovations contribute to investor' returns, there is a question of interest in real estate investment policies and contemporary real estate research. The study aims to assess the extent of innovation diffusion in student housing and its effects on investment returns in Ghana.

The study used a mixed methods approach foregrounded on the innovation diffusion theory. With the mix of surveys and interviews of 828 student housing managers/investors and 25 key student housing association leaders across selected off-campus student housing among six universities in Ghana, the study used both primary and secondary sources. Selection criteria were based on at least one of these criteria: Have operated in the student housing market over the past ten years, have adopted the use of technology in student housing management, have introduced new student housing marketing strategies and have made improvements (added value) to student housing services. Multiple regression and narratives were the main analytical tools employed in this study.

The study demonstrates that over the past ten years, student housing investors in Ghana have invested hugely in product, marketing, process and organisation innovations. Among these innovations, innovations by: marketing through souvenirs and annual-get-togethers product through Internet services processes through Information Management Systems (IMS), and organisation through student leadership were most utilised to descending extent. Furthermore, the study identified marketing and organisation innovation to have the highest effects on investment returns. However, process and product innovation showed a weak and moderate effect on investment returns because management hastily implemented these services without understanding the consequences it has on investment returns in the long run.

The moderate effect of product and process innovation on student housing investment can be a predictor for future student housing investment innovation strategies for new entrants as they do not provide an immediate positive investment return. Key takeaways require management to incrementally implement these innovations and adopt space management practices that create opportunities for future product and process innovations in Ghana. Investors should capitalise on marketing and organisational innovations as the best innovation strategies that yield the highest returns in Ghana.

Student housing investors should focus on emerging student preferences such as entertainment, improved building services and Information Communication to stimulate student housing selection intentions.

Innovation diffusion in student housing is understudied. The closest connection of innovation diffusion theory to product enhancement, marketing and managerial improvement is a strategic tool that facilitates efficiency and productivity in student housing investment.

Residential property in a multi‐asset portfolio context has been considered from two substantially different perspectives: institutional investor's and the household's perspective. This paper constitutes the first of two related surveys on the role of residential property in a multi‐asset portfolio. The paper provides an introduction to housing property investment at a macro level and reviews the main empirical issues related to housing investment in an institutional portfolio context. The literature in this regard generally supports the evidence that residential property is a more effective hedge against inflation than both shares and bonds. Additionally, the reviewed studies generally report that unsecuritised housing investment not only generates risk‐adjusted returns comparable to those of bonds and shares, but also exhibits low levels of correlation with classic asset groups of institutional portfolios.

This second of two related papers in this Journal, reviews empirical evidence available from the literature on the problem of household's optimal portfolio when owner‐occupied housing is included in the list of available assets, namely the risk‐return performance of residential investment, and its usefulness in efficient mixed‐asset portfolios. The risk‐return characteristics of the housing asset is highly dependent on the type of perspective under analysis (household or institutional investor's perspective) and therefore, the two housing investment approaches could lead to different conclusions about the role of housing investment in an portfolio context. The consumption demand for housing together with the markets imperfections place serious constraint on the household's portfolio problem.

The purpose of this paper is to examine whether the house prices in Finland share financial characteristics with assets such as stocks. The studied regions are 15 main regions in Finland over the period of 1988:Q1-2018:Q4. These regions are divided geographically into 45 cities and sub-areas according to their postcode numbers. The studied type of dwellings is apartments (block of flats) divided into one-room, two rooms and more than three rooms apartment types.

Both Ljung–Box and Lagrange multiplier tests are used to test for clustering effects (autoregressive conditional heteroscedasticity effects). For cities and sub-areas with significant clustering effects, the generalized autoregressive conditional heteroscedasticity (GARCH)-in-mean model is used to determine the potential impact that the conditional variance may have on returns. Moreover, the exponential GARCH model is used to examine the possibility of asymmetric effects of shocks on house price volatility. For each apartment type, individual models are estimated; enabling different house price dynamics, and variation of signs and magnitude of different effects across cities and sub-areas.

Results reveal that clustering effects exist in over half of the cities and sub-areas in all studied types of apartments. Moreover, mixed results on the sign of the significant risk-return relationship are observed across cities and sub-areas in all three apartment types. Furthermore, the evidence of the asymmetric impact of shocks on housing volatility is noted in almost all the cities and sub-areas housing markets. These studied volatility properties are further found to differ across cities and sub-areas, and by apartment types.

The existence of these volatility patterns has essential implications, such as investment decision-making and portfolio management. The study outcomes will be used in a forecasting procedure of the volatility dynamics of the studied types of dwellings. The quality of the data limits the analysis and the results of the study.

To the best of the author’s knowledge, this is the first study that evaluates the volatility of the Finnish housing market in general, and by using data on both municipal and geographical level, particularly.

Swiss institutional investors hold approximately 19 per cent of their wealth in property, and the bulk of the allocation to property is housing. The financial reasons which are often given to motivate this investment strategy are twofold. First, property returns are hypothesized to be lowly correlated with the returns on stocks and bonds, and the inclusion of property in portfolios of financial assets should lead to diversification benefits. Second, property is viewed as acting as an effective hedge against inflation. Empirically investigates these two assumptions on the basis of hedonic price indices for Geneva and Zurich apartment buildings. Examines whether an investor who already holds Geneva (Zurich) housing should invest in the other canton. Investigates whether real estate mutual funds should be included in the portfolio in addition to direct real estate holdings. Results suggest that housing is an effective portfolio diversifier but does not provide any better short‐term inflation‐hedging effectiveness than financial assets.

The purpose of this paper is to create an endurance index of housing investor sentiment and use it to forecast housing stock returns. This study performs not only in-sample and out-of-sample forecasting, like many previous studies did, but also a true forecasting by using all lag terms of independent variables. In addition, an evaluation procedure is applied to quantify the quality of forecasts.

Using a binomial probability distribution model, this paper creates an endurance index of housing investor sentiment. The index reflects the probability of the high or low stock price being the close price for the Philadelphia Stock Exchange Housing Sector Index. This housing investor sentiment endurance index directly uses housing stock price differentials to measure housing investor reactions to all relevant news. Empirical results in this study suggest that the index can not only play a significant role in explaining variations in housing stock returns but also have decent forecasting ability.

Results of this study reveal the considerable forecasting ability of the index. Monthly forecasts of housing stock returns have an overall accuracy of 51 per cent, while the overall accuracy of 8-quarter rolling forecasts even reaches 84 per cent. In addition, the index has decent forecasting ability on changes in housing prices as suggested by the strong evidence of one-direction causal relations running from the endurance index to housing prices. However, extreme volatility of housing stock returns may impair the forecasting quality.

The endurance index of housing investor sentiment is easy to construct and use for forecasting housing stock returns. The demonstrated predictability of the index on housing stock returns in this study can have broad implications on housing-related business practices through providing an effective forecasting tool to investors and analysts of housing stocks, as well as housing policy-makers.

Despite different investor sentiment proxies suggested in the previous studies, few of them can effectively predict stock returns, due to some embedded limitations. Many increases and decreases inn prices cancel out each other during the trading day, as many unreliable sentiments cancel out each other. This dynamic process reveals not only investor sentiment but also resilience or endurance of sentiment. It is only long-lasting resilient sentiment that can be built in the closing price. It means that the only feasible way to use investor sentiment contained in stock prices to forecast future stock prices is to detach resilient investor sentiment from stock prices and construct an index of endurance of investor sentiment.

The purpose of this paper is to empirically analyze volatility properties of the house price returns of Turkey and Istanbul, Ankara and Izmir provinces over the period of July 2007-June 2014.

The paper uses conditional variance models, namely, ARCH, GARCH and E-GARCH. As the supportive approach for the discussions, we also use correlation analysis and qualitative inputs.

Empirical findings suggest several points. First, city/country-level house price return volatility series display volatility clustering pattern and therefore volatilities in house price returns are time varying. Second, it seems that there were high (excess) and stable volatility periods during observation term. Third, a significant economic event may change country/city-level volatilities. In this context, the biggest and relatively persistent shock was the lagged negative shocks of global financial crisis. More importantly, short-lived political/economic shocks have not significant impacts on house price return volatilities in Turkey, Istanbul, Ankara and Izmir. Fourth, however, house price return volatilities differ across geographic areas, volatility series may show some co-movement pattern. Fifth, volatility comparison across cities reveal that Izmir shows more excess volatility cases, Ankara recorded the highest volatility point and Istanbul and national series show lower and insignificant volatilities.

The study uses maximum available data and focuses on some house price return volatility patterns. The first implication of the findings is that micro/macro dimensions of house price return volatilities should be carefully analyzed to forecast upside/downside risks of house price returns. Second, defined volatility clustering pattern implies that rate of return of housing investment may show specific patterns in some periods and volatile periods may result in some large losses in the returns. Third, model results generally suggest that however data constraint is a major problem, market participants should analyze regional idiosyncrasies during their decision-making in housing portfolio management. Fourth, because house prices are not sensitive to relatively less structural shocks, housing may represent long-term investment instrument if it provides satisfactory hedging from inflation.

The evidences and implications would be useful for housing market participants aiming to manage/use externalities of housing price movements. From a practical contribution perspective, the study provides a tool that will allow measuring first time of the return volatility patterns of house prices in Turkey and her three biggest provinces. Local level analysis for Istanbul, Ankara and Izmir provinces, as the globally fastest growing cities, would be found specifically interesting by international researchers and practitioner.

Considers whether housing has been a successful investment for the home owner during the period 1984‐1992, in absolute terms and in comparison with other investment media such as equities and gilts. Discusses the social and political influences which have encouraged a rise in home ownership and evaluates the trends in share ownership. Details the methodologies used in calculating the total returns from housing and the impact of taxation. Shows that, on an aggregated UK basis, housing has shown positive overall returns over this holding period and has proved to be a good hedge against inflation, although under‐performing the returns from UK equities. On a regional basis, the housing returns from the northern regions were higher than those from the south of the country, with the latter also showing a higher volatility of return. Raises the question of whether housing could be a worthwhile addition to an institutional property investment portfolio.