价格细分信念与美国房地产繁荣

Federal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print)ISSN 2767-3898 (Online) Price-Segmented Beliefs and the U.S. Housing Boom Margaret M. Jacobson 2026-022 Please cite this paper as:Jacobson, Margaret M. (2026).“Price-Segmented Beliefs and the U.S. Housing Boom,”Finance and Economics Discussion Series 2026-022. Washington: Board of Governors of theFederal Reserve System, https://doi.org/10.17016/FEDS.2026.022. NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment.The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or theBoard of Governors. References in publications to the Finance and Economics Discussion Series (other thanacknowledgement) should be cleared with the author(s) to protect the tentative character of these papers. Price-Segmented Beliefs and the U.S. Housing BoomBy Margaret M. Jacobson∗April 23, 2026Federal Reserve Board Abstract This paper shows that expected capital gains in several MSAs were higher for rel-atively lower-priced, rather than higher-priced, houses during the U.S. boom of the2000s. Because buyers of lower-priced houses tend to be more sensitive to credit con-ditions than buyers of higher-priced houses, this paper documents patterns that areconsistent with an interaction of beliefs and credit conditions in a time period where di-rect evidence on house price beliefs is scarce. Documenting this interaction is importantfor unifying beliefs and credit conditions as joint, instead of competing, explanationsfor the U.S. housing boom of the 2000s. Keywords: housing booms, beliefs, transaction data.JEL: D14, D91, R21, R31 1Introduction Optimistic beliefs and looser credit conditions are two highly studied drivers of the U.S.housing boom of the 2000s.1 While these drivers are often studied in isolation, which al-lows for clean interpretation of mechanisms, studying them as complementary can allow forpropagation dynamics arising from their interaction.2However, studying their interaction ischallenging due to limited data on beliefs prior to 2007.3 This paper finds that beliefs were higher in relatively lower-priced, rather than higher-priced, houses in the 2000s for all locations studied.Since buyers of lower-priced housesare more sensitive to credit conditions (Fidelman and Tapak, 2026), this paper’s evidence ofhigher expected capital gains in the lower-priced housing segment points to an interaction ofbeliefs and credit conditions.4 Interpreted more broadly, this evidence suggests that beliefsand credit conditions were complementary, rather than competing, drivers of the U.S. housingboom of the 2000s. To investigate how beliefs vary across housing price segments, this paper estimates astatistical model of price changes developed by Landvoigt et al. (2015) using Zillow (2026)ZTRAX transaction data on repeat housing sales.Transaction-level data provides repeatsales prices of the same property, which, in turn, allows for the estimation of a commoncomponent of expected capital gains and a cross-sectional dispersion component across typesof houses segmented by price. This model assumes a one-dimensional quality index so thatthe sale price of a house fully reflects its quality. Consequently, as explained by Landvoigtet al. (2015), any statistical model of price changes can give rise to an expected price changeand thus expected capital gains, which can, in turn, be used to proxy for beliefs. By expanding the analysis of Landvoigt et al. (2015) beyond San Diego, CA to includePhoenix, AZ and Cleveland, OH, this paper adds new evidence to the dearth of data onbeliefs in the 2000s.Because the U.S. housing boom of the 2000s varied in timing andmagnitude across metropolitan statistical areas (MSAs), as documented by Ferreira andGyourko (2023), estimating beliefs for multiple MSAs is important for a more comprehensive account of the episode. For this reason, I study three MSAs that capture characteristics ofdifferent submarkets, but all show higher expected capital gains in relatively lower-pricedhouses.San Diego is typically characterized by authors like Saiz (2010) as having a lowelasticity of housing supply such that it is difficult—because of geography—to build morehouses in response to higher house prices. On the other hand, Phoenix and Cleveland areboth characterized as having geography that lends to a higher elasticity of housing supply.However, their fundamentals differ; Phoenix in the 2000s faced rapid housing and populationgrowth and Cleveland depopulation.5 Complementing these within-MSA findings, this paper also estimates substantial varia-tion in expected capital gains over time and across the three MSAs studied, which corrobo-rates the empirical evidence of Soo (2018). 2Estimates of Expected Capital Gains Transactions of repeat sales of single-family homes for the MSAs of Cl