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on Housing and Real Estate |
| By: | Grace Burtenshaw; Ashley Burtenshaw; Meagan Carney |
| Abstract: | In recent years Australia has observed a growing, unexplained resilience of increasing house price trends. Here, we seek to understand what is driving Australia's indestructible asset using insights from market experts. We construct a differential equation model of house price to develop intuition for its historical behaviour and responsiveness to changes in mortgage rates. Using this model, we identify a point of 'decoupling' between house price and mortgage rate in the system with supply limitations found to be the main driver for this change. From there, modern extreme value techniques are implemented on real-world data to investigate how the effectiveness of mortgage rate in moderating extreme house price has changed before and after this historical decoupling. We find that without an increase in the housing supply chain, through either deregulation or reduced competition with government building, an 11\% increase in mortgage rate will be needed to slow extreme housing costs. |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2604.18605 |
| By: | Liping Gao; Ghislain N. Gueye; Hyeongwoo Kim; Jisoo Son |
| Abstract: | Using data from 29 regional housing markets in China, this study examines the long-run relationships between housing prices and key macroeconomic variables. Conventional cointegration methods can be misleading, as estimated coefficients often contradict standard demand–supply theory even when statistical tests indicate cointegration. Among the variables, only real income consistently explains regional housing price dynamics, whereas real interest rates and building costs fail to do so consistently across markets. Region-specific models reveal substantial heterogeneity and are both statistically robust and economically meaningful. Panel cointegration tests that account for cross-sectional dependence fail to detect cointegration when such heterogeneity is ignored. These findings highlight the limitations of uniform national approaches and underscore the need for tailored, region-specific housing policies. |
| Keywords: | Housing Market; Cointegration; Dynamic Oridinary Least Squares; Panel Cointegration Test with CSD; Disaggregated Regional Data |
| JEL: | R30 E00 C51 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:abn:wpaper:auwp2026-03 |
| By: | Michelle Norris (Geary Institute for Public Policy, University College Dublin, Ireland); Lucy O'Hara (Geary Institute for Public Policy, University College Dublin, Ireland); Bob Jordan (Geary Institute for Public Policy, University College Dublin, Ireland) |
| Abstract: | Drawing on policy transfer literature, this paper examines efforts to transfer the cost rental model of affordable housing provision from Austria to Ireland. In examines the motivation for this transfer, the similarities between the Irish and Austrian versions of this model, its effectiveness in the Irish context and the factors that shaped these outcomes. This analysis reveals that as the transfer process progressed the differences between the Irish and Austrian models increased steadily. Many of the adaptations made during the transfer process were necessary to successfully and speedily establish this model in Ireland, where it has provided a successful short-term response to housing unaffordability. However, these adaptations also meant that what had originally envisaged as an ambitious ‘systemic transfer’ (i.e. transfer of the full Austrian cost rental system to drive systemic transformation of Ireland’s ‘dual’ rental market into a ‘unitary’ system, in Kemeny’s conceptualisation), turned into a ‘scheme transfer (i.e. the transfer of parts of the Austrian system to establish an intermediate rental scheme in Ireland). Furthermore, these adaptations reduced the long-term financial sustainability of Ireland’s version of cost renting. On this basis the paper reflects on the challenges of transferring complex, multi-dimensional housing systems compared to singledimensional housing schemes. |
| Keywords: | housing affordability, intermediate renting, cost rents, housing finance. |
| Date: | 2025–10–09 |
| URL: | https://d.repec.org/n?u=RePEc:ucd:wpaper:202505 |
| By: | Binder, Ariel (U.S. Census Bureau); Risch, Max (Tepper School of Business, Carnegie Mellon University); Voorheis, John (Center for Economic Studies, U.S. Census Bureau) |
| Abstract: | Housing is the largest capital asset for most families. While the intergenerational mobility literature has extensively studied the income distribution, it has devoted less attention to housing, in part due to data limitations. We document 3 features of intergenerational mobility by comparing housing capital and income in a new dataset covering 3.4 million U.S. families. First, housing is more persistent across generations than earnings. Moreover, the housing gap between White and Black children grows more sharply throughout the parental resource distribution than does the earnings gap. Second, less than half of intergenerational housing persistence operates through child earnings, leaving substantial scope for direct transmissions of capital assets and knowledge. The direct channel is much weaker among Black families and can almost fully explain their greater risk of downward mobility. Third, local housing supply constraints shape spatial differences in the intergenerational mobility of housing - but not of earnings - as measured in a quasi-experimental shift-share design. Our results highlight a more rigid structure of economic resources across families than implied by income studies. |
| Keywords: | housing markets, intergenerational mobility, homeownership, wealth distribution, capital, income, housing supply, racial disparities |
| JEL: | E24 O18 R31 D31 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18546 |
| By: | Freire Sergio; Pigaiani Cristian (European Commission - JRC); Tucci Michele (European Commission - JRC); Thestrup Sixten; Batista E Silva Filipe (European Commission - JRC) |
| Abstract: | Official statistics depend on traditional methods and primary data sources such as censuses, surveys and administrative data. These ensure consistency and reliability but are generally costly and not very flexible or timely to capture emerging trends or phenomena. The increasing availability of non-traditional data sources offers new opportunities to improve the production and quality of statistics. In the specific context of population and housing statistics, non-traditional data hold potential for filling gaps in official statistics, overcome measurement errors, and provide higher spatial and temporal granularity, enabling more accurate, timely and detailed analyses. This report documents the results of a 2-year project carried by the Joint Research Centre in collaboration with Eurostat entitled ‘Testing the feasibility of using Non-traditional data sources to IMprove Population and Housing Statistics’ (NIMPHS). The main objective was to support Eurostat in exploring the potential and limitations of non-traditional sources such as remote sensing and mobile phone data to improve the quality, detail and/or update of population and housing statistics. In doing so, the JRC also produced and released two new pan-European flagship products by combining existing census statistics and fine-scale data derived from remote sensing: a population grid map at 100 m resolution and a housing grid at 1 km resolution. In addition, the project developed and tested a prototype approach and tool to ‘nowcast’ population grids, and thus improve the timeliness of gridded population data. The report discusses and draws conclusions and implications of the work carried throughout the project. Non-traditional data sources hold substantial potential to improve certain dimensions of pan-European population and housing statistics, namely by increasing their detail, timeliness and overall quality, namely through contributions to quality assurance and control. However, such data sources come with challenges, preventing their direct translation into official statistics. Their use requires good knowledge about the details of the data to devise well-thought data cleaning, processing, and fusion protocols. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:iptwpa:jrc145972 |
| By: | Hyeyoon Jung; Jaehoon (Kyle) Jung |
| Abstract: | Housing is the largest component of assets held by households in the United States, totaling $48 trillion in 2025. When natural disasters strike, the resulting damage to homes can be large relative to households’ liquid savings. Homeowner’s insurance is the primary financial tool households use to protect themselves against property risk. Despite the economic importance of homeowner’s insurance, we know surprisingly little about how insurance contracts are actually designed with respect to property risk. In this post, which is based on our new paper, “Economics of Property Insurance, ” we examine how homeowner’s insurance contracts are structured in practice. Using a new granular dataset covering millions of homeowner’s insurance policies, we document four striking patterns about coverage limits, deductibles, insurance pricing, and the distribution of property losses. |
| Keywords: | insurance; financial constraints; household finance; moral hazard; contracting |
| JEL: | C6 D8 G1 G2 G3 |
| Date: | 2026–04–13 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fednls:103025 |