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on Urban Economics and Policy |
| By: | Julia Fonseca; Lu Liu; Pierre Mabille |
| Abstract: | Mortgage borrowers are "locked in": forgoing moves to hold on to low rates. Lock-in reduces both housing supply, through households who do not sell, and demand, through households who do not buy elsewhere, evidenced by a 40% drop in U.S. existing home sales between 2022 and 2024. We show that mortgage lock-in raises net housing demand: missing downsizers stay in larger homes, particularly in expensive areas, demanding more housing and offsetting a third of the aggregate house price decline caused by higher rates. Using individual-level mortgage data, we provide causal evidence that lock-in disproportionately reduces moves down the housing ladder. We design a spatial housing ladder model with long-term mortgages, which generates a distribution of locked-in rates and a causal effect on mobility consistent with the data, and use it to study the equilibrium effects of lock-in. A temporary rate hike causes lock-in, increasing aggregate house prices by 4.4% and rents by 1.5% relative to a counterfactual without lock-in, while mortgage borrowers' mobility falls by 25%. A $10k tax credit to starter-home sellers modestly increases mobility but raises trade-up home prices. We estimate a cost of $650k per marginal move, indicating that demand-based housing policies are poorly targeted responses to lock-in in our model. |
| JEL: | E21 E44 E52 E61 G5 R20 R3 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35237 |
| By: | Pierre Le Brun (TELEMME - Temps, espaces, langages Europe méridionale-Méditerranée - AMU - Aix Marseille Université - CNRS - Centre National de la Recherche Scientifique, ESPACE - Études des Structures, des Processus d’Adaptation et des Changements de l’Espace - UNS - Université Nice Sophia Antipolis (1965 - 2019) - AU - Avignon Université - AMU - Aix Marseille Université - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur, AU - Avignon Université); Sara Özoğul (University of Groningen [Groningen]); Sarah L Mawhorter (University of Groningen [Groningen]) |
| Abstract: | Fiscal geography illuminates the uneven ramifications of taxation policies on urban and regional development. In terms of housing governance, however, there remains a notable gap in examining how the spatial selectivity of taxation plays out across scales. This article highlights the interconnection between national regulation, housing market dynamics, and local governance systems. It examines how property development and local regulation have adapted to changes following the 2018 Finance Act in France, through which tax incentives for household rental investment were withdrawn from 1, 167 municipalities. We compare the metropolitan regions of Angers and Clermont-Ferrand, two French medium-sized cities. Detailed quantitative analysis shows that the 2018 Finance Act was followed by an increase in property development and prices in eligible municipalities, and a fall or stagnation in those losing eligibility. However, the regions' distinct local-level governance systems, uncovered through policy analysis and 15 interviews with public and private stakeholders, exhibit varying responses to these shifts. We argue that these differences are linked to local political strategies and can influence property development dynamics. The entrepreneurial, pro-development urbanism of Angers adapted through incentive tools aligned with national tax subsidies. In contrast, Clermont-Ferrand's more socially protective managerial urbanism responded by reinforcing regulatory constraints on development. We conclude by highlighting the importance of a multi-scalar perspective in fiscal geography for connecting taxation policies, housing markets, and local governance systems. |
| Keywords: | subsidized housing, housing regulation, property development, local governance, taxes, fiscal geography |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05628919 |
| By: | Mingzhi Xiao; Yuki Takayama |
| Abstract: | Urban spatial structure is commonly evaluated through the spatial distribution of homes and jobs or through aggregate commuting outcomes. Yet these approaches do not reveal how the opportunities created by urban form are selectively transformed into actual residence-workplace connections. This study introduces opportunity-normalized residence-workplace matching by comparing observed commuting distance distributions with opportunity-based distributions constructed from all within-city residence-workplace pairs weighted by residential and employment mass. Using Output Area-level data for nine British cities, we show that realized pairings are systematically more concentrated at shorter distances than the urban opportunity structure alone would predict. After normalization, matching intensity declines with distance in a recurrent but heterogeneous pattern that is approximately linear in log-log space in many cities and can be summarized by a city-specific distance-decay coefficient. London further reveals that this regularity is scale-sensitive: a comparatively flattened citywide pattern separates into consistently negative but heterogeneous relationships across employment-centered subsystems. Supplementary evidence from New York and Chicago shows similar attenuation patterns. These findings identify realized residence-workplace matching as a distinct layer of urban structure and suggest that, in complex metropolitan systems, meaningful spatial regularities may reside in coherent matching fields rather than in aggregate city boundaries. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2606.08207 |
| By: | Valetto, Pietro; Filauro, Stefano; Marx, Ive; Gornick, Janet C. |
| Abstract: | Housing wealth is widely recognized as the most equally distributed major asset class in advanced economies, anchoring middle-class wealth portfolios and overall wealth inequality. Yet, in this paper we observe how treating real estate as a monolith obscures a critical divide. Disaggregating net housing wealth into primary real estate equity and non-primary real estate equity reveals different distributional patterns that fundamentally alter our understanding of housing's role in the wealth distribution. Using harmonized microdata from the EU HFCS (2021) and U.S. SCF (2022), we show that while primary residences are indeed more equally distributed than overall wealth, non-primary real estate such as vacation homes, rental properties, land, and business properties makes up the most highly concentrated component of household portfolios, surpassing even financial assets. Our Gini factor decomposition analysis across various European countries and the U.S. reveals that non-primary real estate exhibits extreme concentration, with Gini coefficients ranging from 0.88 to 0.96, and contributes disproportionately to overall inequality relative to its wealth share. Therefore, the equalizing effect of housing relative to other asset classes emphasized in prior research holds only for owner-occupied homes. These findings suggest that policies treating all housing uniformly may inadvertently amplify wealth concentration, particularly in Europe where non-primary property ownership is nearly twice as prevalent as in the United States. (Stone Center Working Paper Series) |
| Date: | 2026–06–11 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:pyr9t_v1 |
| By: | Bento Maria |
| Abstract: | This paper investigates the spatial concentration of Battery Electric Vehicles (BEVs) across Portuguese municipalities and examines how fiscal incentives are associated with that concentration. Using municipality-level data on BEV registrations, population, density, charging infrastructure, and local parking incentives, this paper combines loglinear modelling, rank-size analysis, and network analysis. Municipalities are represented as nodes in a complete network constructed from geographical distances. A minimum spanning tree is used to identify the connectivity threshold required to keep all municipalities connected. Such a threshold is used to move from a complete network to a sparse one, to which centrality measures are computed. The analysis provides descriptive evidence on whether BEV adoption is concentrated in larger, denser, better connected, and better equipped municipalities. The results contribute to the understanding of spatial inequalities in electric mobility adoption in Portugal and highlight the importance of considering network analysis when evaluating fiscal incentives. |
| Keywords: | Battery electric vehicles, fiscal incentives, network analysis, spatial concentration, road infrastructure, Portugal. |
| JEL: | R12 R48 H23 Q58 C21 |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp04202026 |
| By: | Kuang, Pei; Luca, Davide; Wei, Zhiwu |
| Abstract: | Does political polarisation influence actual economic behaviours? Using British nationally representative surveys and administrative data, we document how the Brexit referendum triggered stark divergences in individual micro and macro expectations between Leave and Remain supporters. Compared to existing research, we show how these polarising effects were driven by a specific policy issue and only marginally related to traditional partisan identities. We also demonstrate how these diverging beliefs influenced major real financial decisions. Leavers became more likely to purchase durable goods and engage in housing transactions, and areas with higher proportions of Leave voters experienced increased housing transaction volumes and rising prices. |
| Keywords: | Brexit; expectations; housing transactions; political polarisation; spending intentions |
| JEL: | D84 E66 P16 R21 |
| Date: | 2026–07–31 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:138681 |
| By: | Yuqi Ma; Zhaoyingzi Dong; Pierre-Alexandre Balland; Hantian Sheng |
| Abstract: | Public procurement of innovation (PPI) is widely regarded as a powerful demand-side policy instrument to stimulate innovation, but its role in shaping regional technological diversification remains unclear. Drawing on evolutionary economic geography, this study examines how PPI affects technological diversification under different spatial sourcing strategies. Using 2.24 million procurement contracts and 2.41 million patents from China (2016-2021), we find that PPI facilitates path-breaking diversification, but mainly through non-local procurement. Compared with local procurement, non-local procurement is more conducive to path-breaking diversification in purchasing regions, while its benefits do not spill over symmetrically to supplying regions. By reconceptualizing PPI as a spatially embedded and relational mechanism, this study extends evolutionary accounts of regional diversification beyond a purely territorial lens and highlights how the spatial organization of public demand shapes uneven opportunities for regional technological development. |
| Keywords: | Public procurement of innovation; Technological diversification; Path-breaking innovation; Non-local procurement; Evolutionary economic geography |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2609 |
| By: | Iyer, S.; Petrukhin, D.; Shrivastava, A. |
| Abstract: | This paper estimates the economic effects of communal riots in India using a new district–year panel for 1994–2013 that combines extended riot data with nightlights, manufacturing outcomes, infrastructure, and district-level credit. To address endogeneity, we use quasi-random variation generated when major Hindu festivals fall on a Friday (Fridays draw large Muslim congregations for weekly prayers) and construct a spatially weighted instrument for local riot risk. Instrumental variable estimates show that higher riot exposure leads to sizable and persistent economic losses. A one-unit increase in distance-weighted exposure reduces nightlights by about 0.105 log points, implying a GDP decline of roughly 0.45%. We trace several channels, including contractions in firm output and employment, fewer bank branches, less agricultural credit, and a thinner market infrastructure. Using the timing of household surveys relative to riots, we also find that trust in state institutions declines. Together, the results quantify the economic costs of communal riots and identify the mechanisms through which social conflict depresses local economic activity. |
| Date: | 2026–06–15 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2644 |
| By: | José Pedro Pontes |
| Abstract: | This paper seeks to provide a reasonable explanation for why private colleges display a much higher elasticity of schooling rates with respect to population density than public universities. It also accounts for the fact that, although private universities played an important role in the early stages of the expansion of higher education in Portugal, their relative weight declined considerably in more recent times. While the higher level of subsidisation of fixed costs in public universities is undoubtedly an important factor behind this pattern, but it is far from the only one. Public universities also tend to internalise spatial knowledge externalities, a behaviour that private institutions do not typically replicate. Consequently, schooling rates in private universities are consistently lower than those in public institutions and this gap narrows as regional accessibility and demographic density increase. Moreover, the evolution of higher education exhibits rising spatial inequalities at earlier stages and diminishing inequalities at later stages. |
| Keywords: | Higher Education Growth, Population Density, Spatial Inequalities in Schooling, Knowledge Spillovers, Public and Private Universities. |
| JEL: | I20 O15 O18 R11 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:ise:remwps:wp04182026 |
| By: | Delgado-Téllez, Mar; Ceglar, Andrej; Spiteri, Sarah; Lebouteiller, Léonore; Vorderobermeier, Nicole |
| Abstract: | Europe is increasingly exposed to heat waves and droughts, but their short-term economic effects across sectors remain hard to predict. This study develops climate-augmented models to predict real growth in per capita value added across 1, 117 EU regions (2002–2022), by combining economic indicators with high-frequency climate data. When using machine learning (ML, Random Forest and XGBoost), climate variables improve predictions in agriculture, while gains for other sectors are limited and do not outperform economic models. Heat wave indicators consistently enhance predictive performance, whereas drought effects vary by sector. Simulations of extreme combined heat and drought scenarios suggest that agricultural annual growth could fall by 1.9 to 7.6 percentage points in most regions, whereas industry, and manufacturing in particular, is less affected, although impacts are more pronounced in Eastern Europe and the Baltic states. Overall, ML models better reflect complex climate–economic interactions, supporting their use for early warning, policy planning, and targeted adaptation. JEL Classification: C53, E37, Q54, R15 |
| Keywords: | climate extremes, machine learning, production, regional predictions |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263248 |