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This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM).

The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices.

The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18.

This study addresses a research gap by using the AHP model to assess the spatial determinants of housing prices in DMA, Saudi Arabia. Few studies have used this model in examining housing price factors, particularly in the context of Saudi Arabia. Consequently, the findings of this study provide unique insights for policymakers, housing developers and other stakeholders in understanding the importance of building size, proximity to transport infrastructure and proximity to amenities in influencing housing prices in DMA. By considering these determinants, stakeholders can make informed decisions to improve housing quality and prices in the region.

This paper explores the real estate price determinants at four geographical levels: in the European Union as a whole, in the 28 European Union countries, in one European Union country (Portugal) and in 25 Portuguese metropolitan statistical areas (MSAs).

The authors run two time series regression models and two panel data regression models with observations of potential real estate price determinants and House Price Indices collected from Eurostat.

The results show that price determinants, such as gross domestic product (GDP), interest rates, housing starts and tourism, are statistically significant, but not in all the four geographical levels of analysis. The results also confirm the autoregressive characteristic of real estate prices, with the last period price change being the most important determinant of current period real estate price change.

Forecasting real estate prices can be made more effective by knowing that each geographical level of analysis implies different price determinants and that momentum is an important determinant in real estate returns.

To the best of the authors knowledge, this is the first study to develop and test a real estate price equilibrium model at several different geographical levels of the same political space.

The purpose of this paper is to investigate the determinants of the affordable housing shortage in the Greater Kuala Lumpur from the suppliers’ perspectives.

Primary data were collected through a cross-sectional survey questionnaire comprising 21 determinants and 111 experts in the housing industry.

The affordable housing shortages are consequences of regulations and policies on land allocations, building materials and the affordable housing market. The government should provide more lands to the developers or the government should directly build affordable housing on their lands. To lower the cost of construction, the government should reduce the importation tax and procedures, and the housing industry should find alternative building materials.

Theoretically, the research provided fresh insights into the causes of housing shortages and reasons for the increase in housing prices. The results will be useful to policymakers towards affordable housing delivery and to the developers and contractors on measures to increase profit margins and increase housing supply.

This paper aims to test the volatility and analyses the macroeconomic determinants of house price volatility in Namibia over the period 2007 Quarter 1 to 2017 Quarter 2. It further explores the causal relations between house price volatility and its determinants.

The study used autoregressive conditional heteroskedastic and generalized autoregressive conditional heteroskedastic models to test for volatility. The vector error correction model was used to analyse the determinants and causal relations.

The results support the hypothesis that house prices in Namibia exhibits persistent volatility. It was further established that past period volatility’ GDP and mortgage loans are the key determinants of house price volatility. Additionally’ there exists unidirectional causality from GDP and mortgage loans to house price volatility.

Policy implications emanating from the study implies that macroeconomic fundamentals should be monitored closely to mitigate the issues of house price volatility.

The study is the first of its kind in Namibia to address the pertinent issues of ever increasing housing prices.

This paper aims to explore factors influencing apartment price volatility in the two biggest cities in Vietnam, Hanoi and Ho Chi Minh City.

The study uses the supply and demand approach and provides a literature review of previous studies to develop four main hypotheses using four determinants of apartment price volatility in Vietnam: gross domestic product (GDP), inflation rate, lending interest rate and construction cost. Subsequently, the Vector Error Correction Model (VECM) is used to analyze a monthly data sample of 117.

The research highlights the important role of construction costs in apartment price volatility in the two largest cities. Moreover, there are significant differences in how all four determinants affect apartment price volatility in the two cities. In addition, there is a long-run relationship between the determinants and apartment price volatility in both Hanoi and Ho Chi Minh City.

Limitations related to data transparency of the real estate industry in Vietnam lead to three main limitations of this paper, including: this paper only collects a sample of 117 valid monthly observations; apartment price volatility is calculated by changes in the apartment price index instead of apartment price standard deviation; and this paper is limited by only four determinants, those being GDP, inflation rate, lending interest rate and construction cost.

The study provides evidence of differences in how the above determinants affect apartment price volatility in Hanoi and Ho Chi Minh City, which helps investors and policymakers to make informed decisions relating to the real estate market in the two biggest cities in Vietnam.

This paper makes several recommendations to policymakers and investors in Vietnam to ensure a stable real estate market, contributing to the stability of the national economy.

This paper provides a new approach using VECM to analyze both long-run and short-run relationships between macroeconomic and sectoral independent variables and apartment price volatility in the two biggest cities in Vietnam.

This study aims to explore empirically the impact of various factors/determinants on housing prices at the country level as well as in Lahore, the most populous metropolitan city of the most populous province Punjab, Pakistan.

This study uses monthly data ranging from 2013M1 to 2020M1 on variables used in the study. Based on the stationarity results, the method of robust least square is used as an estimation technique. The validity of initial results is also authenticated by canonical cointegration regression.

The empirical result reveals that all included variables significantly affect housing prices both at country level as well as in Lahore. This study found negative impact of regressor age, real exchange rate and urbanization on housing prices, whereas the positive impact of gross domestic product (GDP) per capita, foreign remittances, broad money and real interest rate on housing prices in the case of Pakistan was found. On the other hand, results unveiled the negative impact of regressor age (proportion of population aged between 15 and 64), real exchange rate and urbanization on housing prices, whereas the positive impact of GDP per capita, foreign remittances, broad money and real interest rate on housing prices in Lahore metropolitan city was unveiled.

Based on the extant literature survey, this is a more holistic study of its kind that uncovers the macroeconomic determinants by considering the demand side, supply side and demographic factors of escalated housing prices in Pakistan, so that proper policies can be adopted to keep the housing sector stable. Empirical findings are helpful to acquire an enhanced understanding of how the housing price is determined and form a base for government to tackle the housing affordability problem.

This paper aims to reveal major factors affecting housing prices (flats and houses) in Baku, the capital of Azerbaijan Republic.

Based on cross-sectional data set of 497 flats and 443 houses, polynomial regression models are estimated for flats and houses separately. Regression models are estimated by using ordinary least squares.

Location, largeness, repair level and existence of bill of sale are major price determinants for flats. For houses, number of rooms also matters. Findings reveals that houses are land intensive (more floors, less land area) toward city center, and vice versa. Price difference due to existence of bill of sale diminishes significantly toward the surrounding areas.

The data set represents view of sellers and does not take into consideration price bargaining in time of sale; probability of information asymmetries exists which not could accounted for, and urgency of sale is not considered.

Estimation results can be used for housing valuation by real estate market participants and investors.

Research findings reveal importance of bill of sale as a major price determinant and expected to attract policymakers’ attention to solve such a big social problem. Additionally, models can be based for price estimations in Baku housing market.

The study contributes to the literature by empirically analyzing housing market in Baku, Azerbaijan. Research produces new practically valuable findings.

The purpose of this paper is to assess the long-run and short-run drivers of real house prices in Nigeria from 1991Q1 to 2020Q4.

Vector autoregression and cointegration tests were used to assess the key drivers of Nigeria’s real house prices in the long run and short run.

The empirical findings revealed that household disposable income is the most important determinant of house prices in Nigeria. House prices increased by 1.6% and 60.8% in response to a 1% increase in disposable income in the long run and short run, respectively, while real mortgage credits pushed up house prices by 5% and have no long-run effects, suggesting that most Nigerians depend on their money income rather than credits in securing a home. In addition, prices of oil sector products and real interest rates had negative and significant relationship with house prices, while positive correlations were found for real effective exchange rate and real housing investments regardless of the time horizon. The impact of construction costs and cement prices was also documented.

This is likely a pioneering study of its kind to focus on the determinants of real house prices in Nigeria. It is probably the first study, the best of the author’s knowledge, to empirically examine the impact of the oil sector on house prices in the country.

This paper aims to examine real estate price volatility in Hong Kong. Monthly data on housing, offices, retail and factories in Hong Kong were analyzed from February 1993 to February 2019 to test whether volatility clusters are present in the real estate market. Real estate price determinants were also investigated.

Autoregressive conditional heteroscedasticity–Lagrange multiplier test is used to examine the volatility clustering effects in these four kinds of real estate. An autoregressive and moving average model–generalized auto regressive conditional heteroskedasticity (GARCH) model was used to identify real estate price volatility determinants in Hong Kong.

There was volatility clustering in all four kinds of real estate. Determinants of price volatility vary among different types of real estate. In general, housing volatility in Hong Kong is influenced primarily by the foreign exchange rate (both RMB and USD), whereas commercial real estate is largely influenced by unemployment. The results of the exponential GARCH model show that there were no asymmetric effects in the Hong Kong real estate market.

This volatility pattern has important implications for investors and policymakers. Residential and commercial real estate have different volatility determinants; investors may benefit from this when building a portfolio. The analysis and results are limited by the lack of data on real estate price determinants.

To the best of the authors’ knowledge, this paper is the first study that evaluates volatility in the Hong Kong real estate market using the GARCH class model. Also, this paper is the first to investigate commercial real estate price determinants.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.