Housing Demand Research Articles

Pandemic rural residential mobilities? Towards an understanding of who moved house and why in rural Ireland

A growing body of research has emerged in recent years examining the influence of the COVID-19 pandemic on the future of cities, regions and rural places. This includes examining the potential for decentralised housing demand – an urban exodus – as households adapted to a post-pandemic society. This article examines ‘pandemic mobilities’ in the context of rural housing demand in Ireland. Based on survey data across three case study areas, the article compares pre- and post-pandemic ‘movers’ to examine any differences in types of households moving or changes in the motivations of relocating households. Rather than an urban exodus, our research indicates that rural residential mobility is diverse and is often driven by return migration or local mobility, influenced by pre-existing family networks. The pandemic did not appear to be a driver of rural residential change but emerged as an enabling factor (e.g. working remotely) or as an accelerator for planned relocation decisions.

Determining and Quantifying Urban Sprawl Drivers: A Delphi-DANP Approach

Urban sprawl poses a significant and escalating challenge in developing countries, including Iran, leading to substantial transformations in urban areas. Despite efforts to manage urban spatial development, uncontrolled urban sprawl exerts considerable pressure on resources, infrastructure, and the environment. This study aims to identify and quantify the drivers of urban sprawl and investigate their interrelationships within Iranian metropolises. To achieve this objective, the study employs a mixed-method approach, commencing with a review of the existing literature and expert surveys based on PESTEL analysis and the Delphi method. This stage identified and categorized 40 key drivers (sub-factors) into six main categories (factors): political, economic, social, technological, environmental, and legal. Subsequently, the DEMATEL-based Analytic Network Process (DANP) method is utilized to explore the internal interrelationships among factors and sub-factors and to determine their relative weights, offering deeper insights into their relationships and relative importance. The findings reveal a complex interplay of political, economic, social, technological, environmental, and legal factors driving urban sprawl in Iran. Key drivers include political fragmentation, economic competition, social preferences for suburban living, rural-to-urban migration, increasing housing demand, weak legal regulations, natural constraints, inadequate transportation infrastructure, and the impact of technological advancements. Based on these findings, the study recommends a holistic approach to sustainable urban development in Iran, emphasizing the need for stakeholder engagement, participatory decision making, legal reforms, and significant investments in public transportation infrastructure.

INVESTIGATING RESIDENTIAL PROPERTY DEMAND AND SUPPLY DYNAMICS IN LAFIA TOWNSHIP

The study investigated Residential Property Demand and Supply Dynamics in Lafia Township. The study was specifically designed to examine the current demand and supply of residential properties, impact of population growth on housing demand, migration patterns and their influence on the housing market and the affordability of housing for different income groups in Lafia Township. The stratified random sampling and purposive sampling techniques were adopted to select respondents for the quantitative and qualitative methods and structured questionnaire, interview and Focus Groups Discussions were utilized to obtain data to ensure representation across different income groups, geographic areas, and housing types who have relevant knowledge and experience with the housing market in Lafia Township. The Cochran formula was employed to calculate the representative sample size of 385. The qualitative data obtained from in-depth interviews and Focus Groups Discussions were transcribed sorted and classified based on the research questions and the NVIVO software was used in the analyses of the data by systematically categorising the textual data to identify patterns and themes for interpretation and findings written on the basis of the content analysis and presented along with the quantitative results on frequency tables. The study established an imbalance between the demand for and supply of residential properties, population growth in Lafia has been driven by both natural increase and internal migration from rural areas seeking better economic opportunities, a strong positive correlation between population growth and housing demand and majority of residents have moved to Lafia Township within the last five years from surrounding rural areas. The study recommends implementing programmes, aimed at constructing low-cost housing units that meet the needs of low-income families by engaging stakeholders and residents in the housing development process to ensure that new projects meet local needs and preferences while ensuring compliance with building standards.

Judge Dread: Court severity, repossession risk and demand in mortgage and housing markets

Abstract We study the impact of borrower protection on mortgage and housing demand using variation in the likelihood that houses are repossessed coming from heterogeneity in preferences of local judges. We develop a framework that provides conditions to identify the impact of repossession risk on housing and credit demand – holding supply fixed. Empirically, we exploit exogenous variation in risk created by boundaries of courts’ catchment areas. We find that one standard deviation decrease in borrower protection decreases borrowing and house prices by 4.5%.

Greenhouse Gas Emissions and Decarbonization Potential of Global Fired Clay Brick Production.

Fired clay bricks (FCBs) are a dominant building material globally due to their low cost and simplicity of production, especially in low- and middle-income countries. With a projected rising housing demand, commensurate growth in brick demand is anticipated, the production of which could result in significant greenhouse gas (GHG) emissions. Robust models are needed to estimate brick demand and emissions to systematically address decarbonization pathways. Few sources report production values; hence, we present two novel proxy models: (i) a consumption prediction model, relying on country-specific clay extraction data, dynamic building stock modeling, and average material intensity use allowing for projections to 2050; and (ii) a GHG emissions model, using literature-based data and production technology-specific inputs. Based on these models, the current global FCB consumption is estimated as 2.18 Gt annually, resulting in approximately 500 million tCO2e (1% of current global GHG emissions). If unaddressed, this fraction could increase to 3.5-5% in 2050 considering a moderate SSP 2-4.5 climate change mitigation scenario. Consequently, we explored three potential decarbonization pathways: (i) improving energy efficiency; (ii) shifting production to best practices; and (iii) replacing half of FCB demand with hollow concrete blocks, resulting in 27%, 49%, and 51% reduction in GHG emissions, respectively.

Digital Twins and AI Decision Models: Advancing Cost Modelling in Off-Site Construction

The rising demand for housing continues to outpace traditional construction processes, highlighting the need for innovative, efficient, and sustainable delivery models. Off-site construction (OSC) has emerged as a promising alternative, offering faster project timelines and enhanced cost management. However, current research on cost models for OSC, particularly in automating material take-offs and optimising cost performance, remains limited. This study addresses this gap by proposing a new cost model integrating Digital Twin (DT) technology and AI-driven decision models for modular housing in the UK. The research explores the role of DTs in enhancing cost estimation and decision-making processes. By leveraging DTs and AI, the proposed model evaluates the impact of emergent technologies on cost performance, material efficiency, and sustainability across social, environmental, and economic dimensions. As proposed, this integrated approach enables a cost model tailored for OSC systems, providing a data-driven foundation for cost optimisation and material take-offs. The study’s findings highlight the potential of combining DTs and AI decision models to enhance cost modelling in modular construction, offering new capabilities to support sustainable and performance-driven housing delivery. The paper introduces a dynamic, data-driven cost model integrating real-time data acquisition through DTs and AI-powered predictive analytics. This dynamic approach enhances cost accuracy, reduces lifecycle cost variability, and supports adaptive decision-making throughout the OSC project lifecycle.

COVID-19 pandemic and housing demand mortgage: Partial adjustment model approach

One of the efforts to encourage the fulfillment of housing needs is the provision of housing both from the government and the private sector by considering the community's purchasing power. The background of this research is that the fulfillment of housing needs by both the government and the private sector has yet to be maximized while the demand for housing is relatively high. This research is still necessary because the demand for housing is still high. Increased housing demand will push house prices due to limited housing land. The problems faced focus on the relatively high demand for mortgages because even in the conditions of the COVID-19 pandemic, people still make houses their top priority. This study aims to analyze the effect of people's income, loan interest rates, the amount of housing finance, and dummy before and during the COVID-19 pandemic on housing demand mortgage. The data analysis technique in this study used multiple regression Partial Adjustment Model (PAM). The results showed that the variables of income as proxy for GDP and loan interest rates significantly affected housing demand mortgage. In contrast, the amount of housing finance and the COVID-19 dummy had no significant impact. The research implication that researchers can provide to the government is to provide policy recommendations regarding the Housing Finance Liquidity Facility (FLPP) program, which is intended for low-income communities.

Growing demand for housing and the productivity challenges in developing housing projects in Iraq

The housing sector in Iraq is facing growing demand. However, productivity challenges continue to hinder the timely and efficient delivery of residential projects. Recognising the urgent need to increase construction productivity (CP) in this sector, this study investigates critical factors that impact the performance of horizontal housing projects in Iraq. The novelty of this research lies in its focus on housing skeleton tasks, such as foundations, brickwork, and slabs, which are pivotal to project efficiency. A structured questionnaire was developed, encompassing 47 factors distributed across the planning, design, and construction phases. The survey was administered to a range of industry stakeholders, including project managers, consultants, engineers, and supervisors, and it achieved a high response rate of 79.6%. The results were analysed via a modified weighted relative importance index (RII), which incorporates respondents’ experience levels to yield a nuanced understanding of each factor’s impact. The key findings reveal that planning (RII = 0.874), team size (RII = 0.856), quantity of brickwork (RII = 0.838), supervision (RII = 0.815), and unit area (RII = 0.793) are among the most influential factors on CP. These insights provide valuable guidance for researchers and practitioners in identifying areas for strategic improvement, ultimately supporting the capacity of Iraq’s housing sector to meet escalating demands.

Impact of the war in Ukraine on the real estate market in Romania: Analysis in geopolitical context

The war in Ukraine has caused profound transformations in the dynamics of the Romanian real estate market, reflecting the impact of a complex combination of economic and financial, demographic and socio-cultural, as well as political and legal factors. In this context, the objective of the study is to analyze the impact of the war in Ukraine on the real estate market in Romania by assessing the influence of determinants to identify measures to support the stability and development of the real estate market within geopolitical crises. Subsequently, based on statistical analysis methods, the econometric model was developed considering as dependent variables the real estate market (measured by average price for housing) and independent variables – the number of Ukrainian migrants, interest rate on mortgage loans, GDP per capita, and inflation rate. The research results reveal that Ukrainian migration has a significant positive impact on the real estate market, leading to price increases for housing in urban areas and increased demand for rental housing. In addition, mortgage interest rates show a significant negative impact, suggesting that lower affordability of financing limits purchases of real estate. Moreover, GDP growth per capita is positively correlated with price dynamics in the real estate market, confirming that a prosperous economy stimulates investments in real estate. Findings provide valuable insights into understanding how geopolitical and economic factors interact to influence the real estate market.

MODELING HOUSE SELLING PRICES IN JAKARTA AND SOUTH TANGERANG USING MACHINE LEARNING PREDICTION ANALYSIS

The increasing demand for housing in urban agglomerations, particularly in areas like Jakarta, has made homeownership a significant challenge for many, especially first-time buyers and the lower-middle class. Post-pandemic shifts have further influenced housing preferences, driving interest towards suburban areas with green spaces. Despite government efforts through mortgage subsidy programs, affordability remains a concern, particularly in peripheral regions. This study aims to analyze housing prices in various Jakarta regions using machine learning models, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), Light Gradient Boosting Machine (LGBM), and Random Forest. A dataset of 554 house prices from West Jakarta, South Jakarta, Central Jakarta, and South Tangerang was used. The analysis focused on key predictors like land area, building area, bedrooms, and carports, with R² and Mean Squared Error (MSE) metrics evaluating model performance. Results showed that LGBM and Random Forest outperformed others with 0.8 R2 and low MSE, with building and land area as the most significant factors influencing prices. The study concludes that property size is a primary determinant of house prices, and there is a need for policy interventions to make housing more affordable. Additionally, apartment rentals offer a viable alternative, especially in central urban areas, where proximity to economic activities and facilities is crucial. The findings suggest that enhancing marketplace features with predictive tools could further assist buyers in making informed decisions.

Predictive Analytics for Housing Market Trends and Valuation

Introduction: The demand for housing in major cities is exceptionally high due to the concentration of offices and economic hubs in these areas. The combination of limited available land and increased demand drives house prices upward.Objective: To accommodate this, developers are increasingly constructing residential areas on the outskirts of cities, offering easier access to transportation such as trains and highways. These developers compete by offering competitive pricing, diverse housing options, simplified mortgage processes, and attractive promotions like zero down payments. Buying a house is a significant long-term investment, as property values typically appreciate over time. Therefore, a thorough analysis is crucial when purchasing a home. Several key factors, such as location, land size, building area, and property type, play a role in determining house prices.Method: This study adopts a quantitative approach, which involves systematically investigating phenomena by collecting measurable data and analyzing it through statistical, mathematical, or computational methods. Result: This paper discusses the most effective techniques for data collection, pre-processing, feature extraction, model training, and evaluation. The purpose of this research method is to develop theoretical frameworks related to real-world phenomena. Conclusions: Measurement plays a pivotal role in this quantitative study, as it is central to understanding the data and drawing meaningful conclusions. Finally, we evaluate the current state of research, identifying trends and gaps in the field

Income Property in Moscow at the turn of the 19th and 20th centuries as an economic phenomenon

The turn of the 19th to the 20th century marked a period of industrialization and urbanization that significantly impacted social and economic dynamics. Moscow, like other metropolises of the time, experienced rapid urban population growth, creating a demand for both housing and commercial spaces. In this context, studying the emergence and development of income properties is highly relevant as it sheds light on how urban spaces, society, and human relationships to housing transformed under the influence of economic and social changes during this period. This research examines and analyzes the role and place of income properties within the urban housing system, assessing how accessible life in such buildings was for city dwellers, the economic foundations behind their emergence, and the impact of their construction on Moscow's architectural landscape. The conclusion offers a summarized evaluation of the economic, social, and cultural significance of income properties in Moscow. The scientific novelty of this study lies in the underexplored history of Moscow's income properties from financial-economic and social perspectives. Firstly, analyzing financial-economic aspects allows for a deeper understanding of investment mechanisms in urban real estate and their influence on market formation. Investigating the motives and financial strategies of owners and investors in income properties can illuminate the reasons behind the success or failure of such ventures, as well as their role in the city's economic development. Secondly, exploring social aspects enables an examination of issues related to social mobility, population structure, and social changes occurring within the context of urban development. These topics broaden our understanding of the city's social history. Studying these relationships will introduce previously unpublished materials into circulation, systematizing existing data based on diverse sources of information.

The decreasing housing utilization efficiency in China’s cities

‘Ghost cities’ are a well-known phenomenon of (almost) complete vacancy of urban living space in China. Underutilization of urban living space, however, is far more common than complete vacancy. Here we propose the concept of housing utilization efficiency (HUE) and present the following findings: (1) the overall HUE in China’s highly urbanized areas decreased from 84% in 2010 to 78% in 2020, (2) the HUE in central, old urban areas was generally lower than that in the outer layers of urban areas and declined more from 2010 to 2020 and (3) four development types are found to represent different patterns of urban population movement, urban housing growth and HUE change at the intraurban level. These findings provide comprehensive insight into the discrepancies between urban housing supply and demand in China and highlight their connections to the country’s particular urbanization characteristics and policies, which are crucial for future housing development and planning.

The Role of Generative Artificial Intelligence in Solving the Housing Gap in the US for Low-Income Families

The affordable housing crisis in the United States disproportionately affects low-income families, with millions facing severe housing cost burdens and limited access to adequate housing. This article explores the potential of generative artificial intelligence (AI) to address this housing gap by providing innovative, data-driven solutions that optimize housing development, design, and construction processes. By leveraging AI for predictive analysis, automated design, and construction optimization, stakeholders can significantly reduce costs, increase efficiency, and create sustainable housing that meets the needs of low-income populations. The article examines how AI can accurately predict future housing demand, generate efficient building designs, and streamline construction processes, while also addressing challenges such as equity, bias in AI models, and technical and financial barriers to implementation. Additionally, recommendations are provided for policymakers, developers, and community organizations to ensure that AI-driven housing solutions are deployed equitably and effectively. This paper concludes that while AI is not a standalone solution to the housing crisis, it has the potential to complement existing strategies and significantly improve access to affordable housing for low-income families across the U.S.

Social Impact of Converting Rice Field Agricultural Land into Housing

Land conversion refers to the shift in the use or function of land, which has become increasingly common due to population growth and the rising demand for housing, food, and infrastructure. In Pesawaran Regency, particularly in Mada Jaya Village, the conversion of rice fields into housing presents both procedural challenges and significant social impacts. This study examines the process of land conversion and its social consequences, focusing on two key questions: how the conversion process is carried out and what social impacts arise from it. This study applies Talcott Parsons' functional structural theory using the AGIL framework, supported by legal analysis and field studies, to offer practical insights into sustainable land management. The findings reveal that while land conversion should follow legal procedures, such as obtaining a Location Permit (IPPT) and land use transfer permits—discrepancies occur in practice. Some agricultural land has been pre-filled with soil, classifying it as productive land and making it ineligible for conversion permits. The social impacts include reduced food availability, unemployment, and economic instability, despite some landowners benefiting from land sales. To address these challenges, stricter enforcement of regulations, community participation, and support programs for displaced workers are needed. Economic diversification, such as small business development and agro-tourism, is also recommended.

Affordable housing solutions: real estate strategies for addressing urban population growth

Introduction: As the population count in cities increases, the need for affordable and efficient housing systems becomes even more pressing, especially for the poor and middle-income earners. Housing affordability challenges are an important area to which real estate strategies contribute, and this includes funding schemes, resource-efficient construction methods, and a repurposing of structures for different functions. These strategies work towards achieving the goal of closing the deficit of housing availability in the market as well as meeting the necessary criteria for sustainability and habitability of cities of the future. Materials and Methods: The research methodology employed a comprehensive literature review, analysing peer-reviewed articles, industry reports, and case studies from diverse urban contexts. The data collection was majorly centered on successful affordable housing projects, policies, and approaches used in various city settings. Both quantitative and qualitative assessments were used in the studies for comparative analysis of different strategies and solutions with respect to their efficiency and applicability with regard to cost-saving, mass implementation, and social aspects. Finally, a focus on the world of urban planning and real estate with interviews with professionals in these fields as well as policymakers gave an understanding of possible actual difficulties and opportunities of realizing affordable housing strategies. Results: The study revealed several key findings regarding effective real estate strategies for affordable housing. Financing models that were discussed in this paper include public-private partnerships and community land trusts showed promising results regarding the possibility of decreasing housing prices and making housing more accessible. Techno-friendly mechanisms such as modular construction accompanied by the use of lightweight building materials were perceived to be helpful in increasing the rate of construction of houses while reducing the effects of the construction activities on the physical environment. Adaptive reuse mortels demonstrated that movement’s ability to re purpose inactive urban areas as viable affordable housing opportunities satisfied both the demand for housing and site redevelopment. In addition, policy interventions like inclusionary zoning and density bonus were useful in enhancing the private sector’s engagement in affordable housing. Discussion: The research findings highlight the multifaceted nature of affordable housing solutions and the importance of context-specific strategies. Some strategies like public-private partnerships, could be adopted in any city regardless of the type, however the decision on their application depended on the local socio-economic situation and legislation. Effective implementation of sustainable construction depended on appropriate skills by human resources and favourable policies. Architects and developers of adaptive reuse projects encountered issues with code conformity and reception among the population. These findings suggest that the solution to affordable housing must employ the right mix of innovative real estate solutions, supportive public policies, and active community participation. Conclusion: Real estate strategies offer promising avenues for addressing the affordable housing crisis in rapidly growing urban areas. Innovative financing strategies; efficient construction practices; as well as adaptive reuse strategies provide an opportunity for extending the amount of affordable housing in cities alongside sustainable development. However, the accomplishment of these strategies entails cooperation from public, private, and the community sectors

Extend the ProFamy cohort-component method to conduct probabilistic households and living arrangement projections

Abstract In this commentary, we first briefly review the significant utilities of household and living arrangement projections and the main types of methods for conducting household projections. In the second and third sections, we summarize basic ideas, data needed, assessments and applications of ProFamy extended cohort-component methods/software for households and living arrangement projections; and we emphasize the importance to extend the ProFamy methods and software from deterministic to probabilistic households and living arrangement projections. In section 4, we demonstrate that the ProFamy approach provides an adequate and highly feasible modelling framework to extend probabilistic households and living arrangement projections (PHPs), in which the population size/structure projection outcomes are in consistence with those of probabilistic population projections (PPPs) released by United Nations Population Division (UNPD). In the last Section, we discuss and recommend applying the user-friendly R package DemoRates of ProFamy software to estimate rural/urban (or race)-sex-age-specific standard schedules and the demographic summary measures, to conduct analyses and projections, such as single-parent households, caregivers, and care needs/costs for disabled older adults, age-friendly housing and households-based energy demands, etc. for healthy aging and sustainable development studies. Finally, we discuss the prospects of our ongoing international collaborative research project to substantially extend ProFamy cohort-component method from deterministic into probabilistic households and living arrangement projection (PHPs). As compared with ProFamy deterministic projection method, the PHPs produces a lot of additional outcomes of probabilistically projected households and living arrangements in 2021–2100 with uncertainty intervals that are crucial for healthy aging and sustainable development studies.

Housing policy in the Republic of Kazakhstan: problems and possible solutions

In this article, the authors analyzed the main normative legal acts in the field of housing and communal infrastructure. An analysis of existing public housing programs was also carried out, and a number of differences in public housing programs of different years were investigated. The article describes the state of housing legislation of the Republic of Kazakhstan, discusses the provisions of the new Concept of housing and communal infrastructure development for 2023–2029 and its key problems. The authors of the article argue that the housing market is a complex, multifaceted sector with economic, regional, social, cultural, physical, environmental and financial nuances. This is a long-term asset for the population with an often-inelastic supply, when the demand for housing cannot increase the volume of housing construction, as a result of which housing prices rise. A successful public affordable housing policy requires that all these nuances be taken into account on both the supply and demand sides. A public affordable housing policy should include a deep qualitative analysis of the needs and types of housing solutions that would be suitable for different groups of the population in terms of price, location, housing design, size, and so on. Keywords: housing legislation, state housing program, housing law, mortgage, social policy.

Physics-Informed Neural Networks for Heat Pump Load Prediction

Heat pumps are promising solutions for managing the increasing heating demand of residential houses, reducing the environmental impact when used with renewable energy. Accurate heat load predictions allow the heat pump to operate at the most efficient settings, maintaining comfortable temperatures while reducing excess energy use and lowering operating costs. Data-driven prediction solutions may have difficulty capturing the dynamics and nonlinearities of the thermodynamics involved. The physics-informed models combine the monitored observed data with theoretical knowledge of heat pumps and directly integrate physical constraints, allowing for better generalization and reducing the dependence on large volumes of data. However, they require detailed knowledge of the system topology and refrigerant parameters, which increases the model complexity. Therefore, in this paper, we propose a physics-informed neural network for predicting the heat load of heat pumps that integrates thermodynamics directly into the loss function of the neural network. We model the heat load as a function of the input variables, including the inlet temperature, outlet temperature, and water flow rate. We integrate the function during model training to reduce the model complexity. Our approach increases the accuracy of the predictions compared with data-driven models and generates prediction results that are consistent with the actual physical behavior of the heat pump. The results show superior prediction accuracy, with a 7.49% reduction in the RMSE and a 6.49% decrease in the MAPE, while the R2 value shows an increase of 0.02%.

Upzoning and gentrification: Heterogeneous impacts of neighbourhood-level upzoning in New York City

In light of the calls to relax restrictive density regulations, this paper examines how increasing residential development capacity, i.e. upzoning, may change the demographic, socio-economic and housing characteristics of the affected neighbourhoods. We examine the neighbourhood-level upzonings of New York City to answer this question. We find that upzoning is positively associated with signs of gentrification – upzoned neighbourhoods became whiter, more educated and more affluent in the long run. Upzoning is also associated with increases in housing production, but housing prices also increased. Most importantly, we find that these effects varied significantly by the intensity of upzoning and the pre-upzoning local contexts. Neighbourhoods affected by intense upzonings experienced gentrification more intensely, along with greater housing production, rent growth and housing price appreciation. Black-majority and low-income neighbourhoods experienced gentrification to the greatest extent, while neighbourhoods with high demand for housing saw the greatest increases in housing supply. We discuss different mechanisms of gentrification likely at play for the different types of neighbourhoods.