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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.

An accurate predictive model for forecasting urban housing price in Isfahan can be useful for sellers and owners to take more appropriate actions about housing supplying. Also, it can help urban housing planners and policymakers in managing of the housing market and preventing an urban housing crisis in Isfahan. The purpose of this paper is forecasting housing price in Isfahan city of Iran until 2022 using group method of data handling (GMDH).

This paper presents an accurate predictive model by applying the GMDH algorithm by using GMDH-Shell software for forecasting housing price in municipal boroughs of Isfahan city till the second half of 2022 based on creating time series and existing data. Alongside housing price, some other affecting factors have been also considered to control the forecasting process and make it more accurate. Furthermore, this research shows the housing price changes of boroughs on map using ArcMap.

Based on forecasting results, the housing price will increase at all boroughs of Isfahan till second half of the year 2022. Amongst them, Borough 15 will have the highest percentage of the price increasing (28.27%) to year 2022 and Borough 6 will have the lowest percentage of the price increasing (8.34%) to the year 2022. About ranking of the boroughs in terms of housing price, Borough number 6 and 3 will keep their current position at the top and Borough number 15 will stay at the bottom.

In this research, just few factors have been selected alongside housing price to control the forecasting process owing to limitation of reliable data availability about affecting factors.

The most remarkable point of this paper is reaching to a mathematical formula that can accurately forecast housing price in Isfahan city which has been rarely investigated in former studies, especially in simplified form. The technique used in this paper to forecast housing price in Isfahan city of Iran can be useful for other cities too.

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

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.

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

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.

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.

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.

The purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns and volatility.

The competing models are the autoregressive moving average (ARMA) model and autoregressive fractional integrated moving average (ARFIMA) model for house price returns. For house price volatility, the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model is competing with the fractional integrated GARCH (FIGARCH) and component GARCH (CGARCH) models.

Results reveal that, for modelling Finnish house price returns, the data set under study drives the performance of ARMA or ARFIMA model. The EGARCH model stands as the leading model for Finnish house price volatility modelling. The long memory models (ARFIMA, CGARCH and FIGARCH) provide superior out-of-sample forecasts for house price returns and volatility; they outperform their short memory counterparts in most regions. Additionally, the models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances.

The research results have vital implications, namely, portfolio allocation, investment risk assessment and decision-making.

To the best of the author’s knowledge, for Finland, there has yet to be empirical forecasting of either house price returns or/and volatility. Therefore, this study aims to bridge that gap by comparing different models’ performance in modelling, as well as forecasting the house price returns and volatility of the studied market.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used 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. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

This research developed a model to understand and predict housing market dynamics and evaluate the significance of house permits data in the model’s forecasting capability.

The research uses a multilevel algorithm consisting of a machine-learning regression model to predict the independent variables and another regressor to predict the dependent variable using the forecasted independent variables.

The research establishes a statistically significant relationship between housing permits and house prices. The novel approach discussed in this paper has significantly higher prediction capabilities than a traditional regression model in forecasting monthly average prices (R-squared value: 0.5993), house price index prices (R-squared value: 0.99) and house sales prices (R-squared value: 0.7839).

The impact of supply, demand and socioeconomic factors will differ in various regions. The forecasting capability and significance of the independent variables can vary, but the methodology can still be applicable when provided with the considered variables in the model.

The resulting model is helpful in the decision-making process for investments, house purchases and construction as the housing demand increases across various cities. The methodology can benefit multiple players, including the government, real estate investors, homebuyers and construction companies.

Existing algorithms and models do not consider the number of new house constructions, monthly sales and inventory in the real estate market, especially in the United States. This research aims to address these shortcomings using current socioeconomic indicators, permits, monthly real estate data and population information to predict house prices and inventory.

The purpose of this paper is to estimate long‐run elasticities for housing prices in Tehran's (capital of Iran) 20 different zones relative to several explanatory variables available for use such as land price, total substructure area, material price, etc. Moreover, another goal of this paper is to propose a new approach to deal with problems which arise due to a lack of proper data.

The data set is gathered from “The Municipality of Tehran” and “The Central Bank of Islamic Republic of Iran (CBI)”. One‐way fixed effects and one‐way random effects approaches (which are panel data approaches) are applied to model housing price forecasting function in Tehran's 20 different zones. Results are compared with ordinary least squares approach which is a common approach in this field. Finally, outcomes of the preferred approach are discussed and analyzed with regard to the economic point of view.

Results show that one‐way fixed effects approach provides more accurate forecasts and can be considered as a suitable tool to deal with housing price forecasting problems in environments which are: uncertain, complex, and faced with a lack of proper data. Moreover, it is found that land price is the most effective factor that has impact on total housing cost in Tehran, i.e. the main portion of house prices in Tehran is affected by land price, so appropriate policies have to be made by the government to control fluctuations of this factor.

The proposed approach will supply policy makers with improved estimations with decreased errors in uncertain and complex environments which are faced with a lack of proper data, and it extracts valuable information which enables policy makers for handling non‐linearity, complexity, as well as uncertainty that may exist in actual data sets with respect to housing price forecasting. Moreover, the proposed approach can be applied to similar housing price case studies to obtain more accurate and more reliable outcomes.

Applying panel data approach for estimation of housing prices is relatively new in the field of housing economics. Moreover, this is the first study which employs panel data approach for analyzing the housing market in Tehran.

House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.

This paper aims to investigate Lithuanian house price changes. Its twin motivations are the importance of information on future house price movements to sector stakeholders and the limited number of related Lithuanian property market studies.

The study employs ARIMA modelling approach. It assesses whether past is a good predictor of the future. It then examines issues relating to an application of this univariate time-series modelling technique in a forecasting context.

As the results of the study suggest, ARIMA is a useful technique to assess broad market price changes. Government and central bank can use ARIMA modelling approach to forecast national house price inflation. Developers can employ this methodology to drive successful house-building programme. Investor can incorporate forecasts from ARIMA models into investment strategy for timing purposes.

Certainly, there are number of limitations attached to this particular modelling approach. Firm predictions about house price movements are also a challenge, as well as more research needs to be done in establishing a dynamic interrelationship between macro variables and the Lithuanian housing market.

Although the research focused on Lithuania, the findings extend to global housing market. ARIMA house price modelling provides insights for a spectrum of stakeholders. The use of this modelling approach can be employed to improve monetary policy oversight, facilitate planning for infrastructure or social housing as a countercyclical policy and mitigate risk for investors. What is more, a greater appreciation of Lithuania housing market can act as a bellwether for real estate markets in other trade-exposed small country economies.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.