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This paper aims to bring together research on housing market area, submarket and household migration into a systems approach that helps us gain a better understanding of the structure and dynamics of a housing market and identify housing problems for a large metropolitan area.

The paper uses a geographic information system (GIS)-based method with simple quantitative techniques, including spatial analysis, location analysis, house price clustering and cross-tabulation. The analysis is based on migration data from the 2011 Census, house price data from the Land Registry in 2011 for Greater Manchester at the ward level and the output areas level.

The results show that different submarkets and housing market areas had different patterns of spatial migration and connections with other areas. Through a systematic analysis of migration and house price in combination, it also found a close connection between destination submarkets and the ages of migrants and identified specific problematic patterns for a large metropolitan area.

The interactions between the owner-occupied sector and the social and private rented sectors are arguably an important omission from the analysis. Also, it is acknowledged that clustering ward units based on price differentials is subject to distortions in terms of specification, size and shape. Moreover, the use of the large samples may result in very small p-values, leading to the problem of the rejection of the predefined hypothesis.

A systematic analysis of migration and house price in combination may be used to gain a better understanding of the housing market dynamics and identify housing problems systematically for a large metropolitan. It may help to identify low-demand areas, high-demand areas and assist planners with decisions in allocating suitable land for new housing constructions.

The GIS-based method introduced in the paper could be considered as an effective approach to provide a better basis for determining policy interventions and public investment designed to allocate land resources effectively and improve transport systems to change existing problematic migration patterns.

This paper fills a gap in the international literature in relation to adopting a systems approach that analyses migration and house price data sets in combination to systematically explore migration patterns and linkages and identify housing problems for a large metropolitan area. This systems approach can be applied in any metropolitan area where migration and house price data are available.

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.

The purpose of this paper is to seek to drive the modernization of the entire national economy and maintain in the long-term stability of the whole society; this paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem.

This paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem. First, a novel variable SW evaluation algorithm is proposed based on the sensitivity analysis, and then the grey relational analysis (GRA) algorithm is utilized to select influencing factors of the commodity housing market. Finally, the AWGM (1, N) model is established to predict the housing demand.

This paper selects seven factors to predict the housing demand and find out the order of grey relational ranked from large to small: the completed area of the commodity housing> the per capita housing area> the one-year lending rate> the nonagricultural population > GDP > average price of the commodity housing > per capita disposable income.

The model constructed in the paper can be effectively applied to the analysis and prediction of Chinese real estate market scientifically and reasonably.

The factors of the commodity housing market in Wuhan are considered as an example to analyze the sales area of the commodity housing from 2015 to 2017 and predict its trend from 2018 to 2019. The comparison between demand for the commodity housing actual value and one for model predicted value is capability to verify the effectiveness of the authors’ proposed algorithm.

In India – the largest democracy and second most populated country globally – the housing research domain is relatively under-researched and under-theorized. To support and advance research in this domain, this study aims to form and organize the repository of extant academic knowledge in the subject matter of housing research in India.

This study uses a scoping review methodology and a thematic analysis method. All the articles analyzed in this study were systematically searched by following the scoping review approach proposed by Arksey and O’Malley (2005). An initial search found 365 articles and finally, 108 articles that met the inclusion criteria were analyzed using the thematic analysis method.

The data extracted from these 108 articles were analyzed using thematic analysis to arrive at four thematic areas, namely, housing policy, slum housing, housing finance and affordable housing. These thematic areas and 11 sub-themes present under them were used to present a thematic map of housing policy research in India.

This paper contributes to presenting an up-to-date literature review of the housing policy research in India.

To the best of our knowledge, this scoping review focused on housing research in India is the first of its kind. We hope that this study provides a repository of extant research on housing research in India to help current and future researchers.

This paper aims to provide a comprehensive bibliometric study of housing prices according to the articles collected by the Web of Science (WOS).

This paper studies 4,125 research papers on housing prices in the core collection database of WOS. Using VOSviewer, this paper makes a bibliometric and visual analysis of the housing prices research from 1960 to 2020 and probes into the housing prices research from five aspects: time, international cooperation, institutions author cooperation and research focuses.

Keywords such as influencing factors of housing prices, analysis of supply and demand, policy and housing prices and regional cities appear frequently, which indicates the main direction of housing price research literature. Recent common keywords include regression analysis and house price forecast. Countries, like the USA started early in the study of housing prices, and the means and methods in the field of housing price research are mature, leading the forefront of housing price research. Compared with the USA and other Western developed countries, the housing price research in developing countries needs to use innovative research methods and put more effort on sustainability. Research shows that housing price is closely related to economy, and keyword cluster analysis shows that gross domestic product, interest rate, currency and other keywords related to economy are of high-frequency.

This paper only uses articles from one database (WOS), which does not represent all research papers published worldwide. Some studies have been published for a long time, and the reference value to the research focuses and future research might be limited. There are many kinds of journals included in the study with different publishing frequencies, time ranges and numbers of papers. These may have some influence on the research results.

The main theoretical contribution of this paper is to supplement the current academic research on housing prices. This paper reveals the key points of housing prices research and possible research problems that need attention. We can know from the future research direction and practice which can offer insights for future innovative direction.

The purpose of this paper is to compare the different preferences between property practitioners and residential consumers on housing prices in the Jakarta Metropolitan Region.

The Jakarta Metropolitan Region as the largest metropolitan city in Indonesia was selected as the main sample city for this study. This study comprises 134 respondents from property practitioners and 277 respondents from residential consumers. Data were collected from all regions in Jakarta Metropolitan Region and their respective satellite cities. Descriptive analysis, the correlation study, Wilcoxon t-test and principal component analysis were used to compare the findings between each group’s preferences on housing attributes.

The results of this research provide an analysis on the different decisive attributes for each group, disparities on the correlation between attributes in housing consumers and property practitioners and disagreements among each group on the attribute preferences influencing housing prices in the Jakarta Metropolitan Region.

In conclusion, the study provides valid and dependable evidence on different consumers and property practitioners attribute preferences for housing products in the Jakarta Metropolitan Region.

This research is the first to compare the attribute preferences for housing products between housing consumers and property practitioners in Indonesia. In addition, this study is one of the first to reaffirm preference attributes influencing housing product prices in Indonesia.

Information about the current and future composition of the population in terms of household size and the desired housing preferences provides a good foundation for determining current and future housing needs. The policy-makers and developers can also use such knowledge as a starting point in their housing and commercial real estate investment decisions. In Turkey, urbanization and housing issues have accompanied the growth of industrialization. Within the scope of the country’s urbanization history, various instruments have been used to solve the lack of housing issues. The constructed houses should be accessible or affordable by fixed-income earners in the middle and lower socio-economic classes, who are mostly excluded. In particular, the real estate development sector has taken manageable risks by closely following the changing social and economic conditions and developing a variety of housing concepts. The purpose of this paper is to investigate the housing sector situation and affordability issues and then use time series analysis to present relationships between macroeconomic factors and housing demand in Ankara region.

The approach uses a survey of recent housing projects cover 2016 to 2018 for housing affordability conditions. Also, the study uses the Johansen co-integration test, variance analysis and impulse-response test to explain the relationships between macroeconomic indicators and housing demand for Ankara.

According to the results of time series analysis, the macroeconomic factors are affecting the demand and the number of houses sold. The research results try to find a negative or positive correlation between the numbers of houses sold and the monthly macroeconomic variables. Mortgage interest rates, usage permits, construction permits and household expenditure were found the most correlated with housing sold as a representative proxy of housing demand. This paper claims that current housing affordability is related to current housing supply and demand variables. If housing supply (as construction and usage permits) and income (as interest rates and expenditures) are at favorable levels, then housing transaction volumes increase.

This paper highlights the need to examine how to assist developers to more rapidly develop knowledge and experience to reflect the implications of change in practice. This paper is formulating a housing demand model for real estate developers, using number of house sales and other administrative statistics in Ankara region.

If macroeconomic conditions are stable, then this encourages consumers to invest for housing whether they are affordable or not. According to the results, key factors of housing market are based on interest rates, income expectation and gaining social status. The consumers anymore not only want to buy a house to live and also want to gaining prestige.

The paper not only shows that current price is affordable or not but also supports why price is going up although price is not affordable. The findings identify how the market is developing and adhering to a product model development theory. The paper is different from previous studies because of the use of monthly income and supply proxies together in Turkey with time series model. These results are close to the theoretical expectations and provide good indicators for policy-makers.

This paper aims to extend existing research in relation to both the importance of volume effects within housing markets and the specific behaviour of the London housing market. A detailed borough-level examination is undertaken of the relationships between volume, house prices and house price volatility. Support for alternative housing market theories, the degree of heterogeneity in house price behaviour across boroughs and the extent to which housing displays differing properties to other financial assets are examined.

Correlation analyses, causality testing and volatility modelling are undertaken in extended forms which synthesise and extend approaches within the housing, economics and finance literatures. The various modelling and testing techniques are supplemented via the use of alternative variable transformations to evaluate housing market behaviour in detail.

Novel findings are provided concerning both volume effects within housing markets generally and the specific properties of London housing market. Evidence concerning bubbles, the volatility-reducing effects of volume, the importance of geographical and price-related factors underlying the relationship between volume and both house price growth and volatility and the presence of asymmetric adjustment in the London housing market are all provided. The extent and nature of the support available for alternative housing market theories are evaluated.

The volatility-reducing effects of volume within housing markets, along with volume effects and the presence of asymmetric adjustment within the London housing market are examined for the first time. New empirical evidence on the support for alternative housing market theories and the differing empirical characteristics of housing relative to other financial assets are presented.

The process for examining the value of house prices in an urban city has given limited attention, if any, to demographic variables associated with urban geography. Although the disciplines of property/real estate and demography have moved closer, little progress has been made when modelling house prices using population-related data in the field of urban geography to explain the level of house prices.

This paper proposes an innovative model to examine the influence of population variables on the level of house prices. It used a two-stage approach as follows: principal components analysis (PCA) identified social dimensions from a range of demographic variables, which were then retained for further analysis. This information was sourced from two Australian Bureau of Statistics censuses undertaken involving all Melbourne residents during 1996, 2001, 2006 and 2011; multiple regression analysis examined the relationship between the retained factor scores from the PCA (as independent variables) and established residential house prices (as the dependent variable).

The findings confirm the demographic profile of each household, which is directly related to their decisions about housing location and house prices. Based on a case study of Melbourne, Victoria, it was demonstrated that households with specific demographic characteristics are closely related to a certain level of house prices at the suburban level.

This is an innovative study which has not been previously undertaken for an extended period of time to facilitate an analysis of change over time.

This study aims to address the factors or attributes that would influence the price of residential products in Jakarta Metropolitan Region.

In total, 202 respondents from all across Jakarta Metropolitan Region participated in the questionnaire for this study. Demographic questions are categorized into age, gender and preferences for real estate locations. The questionnaire was made based on the author’s previous studies. Of the total respondents, 127 were males and 75 were females with age ranging from 18 to 56 years old. For data analysis, the authors utilized factor analysis, Cronbach’ α test and analysis of correlation to reach the conclusion of this study.

The findings suggested that from the initial three factors groups, there are five new groups that emerge as influencing factors for housing prices. Cronbach’ α score were verified (α = 0.906). Correlation study result suggested that the initial three factors groups produce a significant correlation between each of them, except for the factor of “overall location” and “located near family.” After factor analysis, the research results show that there are two new additional groups of factors that emerge as influences to housing prices. There are significant scores of differences between gender and real estate location preference toward the groups of factors.

This study shows how physical qualities, concept and location factors influence the housing price perception of their consumers. The result shows to be relatively reliable and valid.

The study is the first to analyze the relationship between the factors for preferences on residential products and housing price in Indonesia. This paper is also intended to be the first to pioneer the study on factors of preferences on residential products in Indonesia. The findings will be useful to develop pricing models for housing product in Indonesia.