Housing Booms and Local Capital Misallocation: Liu, Zhao & Zhao (2026)

What this is. The paper’s core results, the economic mechanism (crowding-out of firm credit via housing booms and the LGFV government-loan channel), and the DiD/event-study design with the TFP aggregation equation: enough to know what it found and how, without reading all 16 pages. To replicate or extend, read the full source at the original.

TL;DR

Liu, Zhao, and Zhao (2026) use China’s 2010-11 housing purchase restriction (HPR) policy as a natural experiment to study how housing booms affect manufacturing-firm credit. The policy restricted household purchases (in most cities, to two homes with a minimum two-year gap), reducing housing demand and prices. Comparing 45 treated cities with 216 controls over 2006-2013, the paper finds that HPR reduced household mortgage loans and local-government/LGFV loans, freeing bank credit for firms. The preferred estimates show firm leverage rising by 3.78 percentage points and interest rates falling by 1.62 percentage points. Smaller firms, firms with higher initial MRPK, and firms in cities with more constrained banks benefited more. Greater credit access accompanied increases in non-housing investment, output, and value-added. Within-city MRPK dispersion fell significantly. The authors estimate a 2.04% industrial TFP gain using their allocation formula; a separate reduced-form calculation implies 3.1%. Their evidence supports crowding-out as the dominant channel in China while also finding evidence consistent with a collateral channel.

Core results

Magnitudes and significance are as reported; **/*** = 5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	HPR policy significantly raised firm leverage in treated cities	Table 1, col. 2, p. 6	DiD coeff. 0.0378*** (se 0.0099); leverage = debt / total assets
R2	HPR policy significantly lowered firm interest rate in treated cities	Table 1, col. 7, p. 6	DiD coeff. -0.0162*** (se 0.0034); interest rate = total interest / total debt
R3	Firms increased non-housing investment and industrial output after HPR	Table 2, cols. 1, 4-5, p. 7	I(non-housing investment > 0): 0.0302** (se 0.0119); log output: 0.1081** (se 0.0529); log value-added: 0.1409** (se 0.0679)
R4	HPR policy significantly reduced household mortgage loans	Table 5, Panel A, col. 5, p. 8	DiD coeff. on log total mortgage loans: -0.2893*** (se 0.0751)
R5	HPR policy significantly reduced local government (LGFV) loans	Table 5, Panel B, col. 13, p. 8	DiD coeff. on log total LGFV loans: -0.3152** (se 0.1564)
R6	HPR policy significantly reduced within-city cross-firm MRPK dispersion	Figure 5a, 5c, 5e, p. 13	Event-study coefficients on Var(log-MRPK), 90th/10th, and 75th/25th MRPK ratios all significantly negative post-HPR
R7	HPR policy increased estimated aggregate industrial TFP	Section 5.5, Eq. 5, p. 12-13; Table A.21, p. 13	Allocation formula: 2.04%; separate reduced-form estimate: treated-city TFP gain of ~6% x 51% national capital share = ~3.1%
R8	HPR increased firms’ non-housing investment	Table 2, col. 2, p. 7	DiD coeff. on log non-housing investment: 0.1768** (se 0.0826)
R9	HPR increased firms’ net non-housing capital	Table 2, col. 3, p. 7	DiD coeff. on log non-housing capital: 0.1615* (se 0.0880)
R10	HPR reduced secondary-market housing prices and transaction volume	Table 5, Panel A, cols. 2 and 4, p. 8	Log average housing price: -0.0444** (se 0.0172); log transaction area: -0.3420*** (se 0.0484)
R11	HPR reduced primary-market land prices and area sold	Table 5, Panel B, cols. 8 and 10, p. 8	Log land price: -0.3874*** (se 0.1078); log transaction area: -0.3988*** (se 0.1215)
R12	HPR reduced local governments’ land-sales revenue	Table 5, Panel B, col. 12, p. 8	DiD coeff. on log land-sales revenue: -0.4300*** (se 0.0673)
R13	Firm credit access gains were larger where banks had higher leverage	Table 6, cols. 3 and 6, p. 10	Triple interaction: leverage 0.0952*** (se 0.0233); interest rate -0.0316*** (se 0.0081)
R14	Firm credit access gains were larger where banks had lower deposits	Table 7, cols. 3 and 6, p. 14	Triple interaction: leverage 0.0558*** (se 0.0152); interest rate -0.0201*** (se 0.0058)
R15	Smaller firms by initial assets benefited more from HPR	Table 8, cols. 1-2, p. 14	Treat x Post x log assets: leverage -0.0155*** (se 0.0039); interest rate 0.0053*** (se 0.0014)
R16	Smaller firms by initial employment benefited more from HPR	Table 8, cols. 3-4, p. 14	Treat x Post x log employment: leverage -0.0151*** (se 0.0038); interest rate 0.0033** (se 0.0014)
R17	Property-owning firms benefited less, consistent with a collateral channel	Table 8, cols. 5-6, p. 14; discussion p. 11	Treat x Post x property holding: leverage -0.0217*** (se 0.0053); interest rate 0.0081*** (se 0.0018)
R18	Firms with higher initial MRPK benefited more from HPR	Table 8 (continued), cols. 7-8, p. 15	Treat x Post x 2009 MRPK: leverage 0.0084** (se 0.0035); interest rate -0.0032** (se 0.0014)
R19	Event studies show no differential pre-trends in core outcomes	Figures 2 and A.4, pp. 8-9; Figure 3, p. 9	No differential pre-treatment estimates reported for the main firm outcomes or mortgage and government loans; year 0 omitted because 2010 ASIF data are unavailable
R20	Credit-access results hold using neighboring cities as controls	Table 3, cols. 1 and 5, p. 7	Leverage: 0.0364*** (se 0.0104); interest rate: -0.0131*** (se 0.0036)
R21	Credit-access results hold in the full ASIF sample	Table 4, cols. 1 and 5, p. 8	Leverage: 0.0381*** (se 0.0099); interest rate: -0.0153*** (se 0.0032)
R22	Reduced-form estimates imply a 3.1% national industrial TFP gain	Section 5.5, Table A.21, p. 13	Treated-city industrial TFP rose approximately 6%; scaled by 51% national capital share: 6% x 51% = 3.1%
R23	HPR had smaller and less significant effects on labor misallocation than on capital misallocation	Figure 5, panels b, d, f, p. 13	MRPL dispersion estimates are smaller and less significant than the corresponding MRPK dispersion estimates
R24	Credit-access gains were strongest for private firms across ownership groups	Table 1, cols. 3-5 and 8-10, p. 6	Leverage: private 0.0462*** (se 0.0121), state-owned -0.0032 (se 0.0135), foreign 0.0143* (se 0.0082); interest rate: private -0.0158*** (se 0.0036), state-owned -0.0071*** (se 0.0024), foreign -0.0105*** (se 0.0031)

Overall (paper’s conclusion). China’s prolonged housing boom generated negative externalities on manufacturing firms by crowding out bank credit through household mortgage lending and local-government borrowing (including LGFV loans backed by land-sale revenue). The HPR-induced housing-market decline reduced these competing loans, improved firm credit access and capital allocation, and raised estimated industrial productivity. The allocation formula estimates a 2.04% TFP gain; the separate reduced-form approach implies 3.1%. The authors also find evidence consistent with a collateral channel, though their results support crowding-out as dominant in China.

Theory / model

The paper presents no formal general-equilibrium model. The theoretical argument rests on two institutional features of China’s credit market that create the conditions for housing-boom crowding-out.

Geographic segmentation. China’s credit market is highly segmented geographically: about 89% of all bank loans are issued to borrowers in the issuing bank’s own city (Gao et al., 2019, cited p. 3). When local housing demand rises, local banks increase mortgage lending to households and extend more credit to LGFVs (whose borrowing capacity is backed by rising land-sales collateral and land-conveyance revenue). Because banks face binding regulatory constraints (Basel III leverage ratio, loan-to-deposit ratio, and annual credit quotas from the People’s Bank of China), credit to housing-related borrowers crowds out credit to local manufacturing firms.

The paper follows Chakraborty, Goldstein, and MacKinlay (2018) in examining the US crowding-out channel and adds the LGFV government-loan mechanism in the Chinese setting.

Identification. The HPR policy was implemented in 46 cities, with rapid housing-price growth the primary selection factor. The authors use a DiD design and report no differential pre-existing trends in leverage, interest rate, investment, or output (Figure 2 and Figure A.4, pp. 9-10). The policy had no explicit government mandate to redirect lending to industry (Section C of the online appendix), supporting their interpretation that effects operate through housing and credit markets.

Tested hypotheses. Three primary hypotheses follow:

1. Housing market declines raise firm credit access: leverage rises, interest rate falls.
2. The mechanism operates through reductions in household mortgage loans and LGFV loans, crowding in credit to manufacturing firms.
3. Effects are stronger in cities with more financially constrained banks (higher bank leverage, lower deposits) and in firms that are more ex-ante credit-constrained (smaller, lower MRPK).

Hsieh and Klenow (2009) provide the theoretical framework for how credit-access heterogeneity translates into capital misallocation: firms with high marginal revenue product of capital (MRPK) relative to the average are under-financed, and equalizing credit access across firms reduces the MRPK dispersion and raises aggregate TFP.

Method

The headline estimator is a two-way fixed-effects DiD regression (p. 5):

$$y_{i,c,t} = \beta \, \text{Treat}_c \times \text{Post}_t + \lambda X_{i,t} + \theta X_{c,t} + \gamma_i + \gamma_t + \epsilon_{i,c,t} \tag{1}$$

where $y_{i,c,t}$ is leverage or interest rate of firm $i$ in city $c$ in year $t$; $\text{Treat}_c$ is a dummy equal to 1 for the 45 HPR-implementing cities; $\text{Post}_t$ is equal to 1 for years 2011 onward; $\gamma_i$ and $\gamma_t$ are firm and year fixed effects; $X_{i,t}$ includes firm age, age squared, log assets, and fixed-asset share; $X_{c,t}$ includes city-level contemporaneous real-estate investment share, total credit/GDP, log real GDP, and GDP growth rate. Standard errors are clustered at the city level.

Dynamic (event-study) effects use (p. 5):

$$y_{i,c,t} = \sum_{\tau=-4}^{3} \beta_\tau \, \text{Treat}_c \times I_t^\tau + \lambda X_{i,t} + \theta X_{c,t} + \gamma_i + \gamma_t + \epsilon_{i,c,t} \tag{2}$$

where $I_t^\tau = 1$ if year $t$ is $\tau$ years after the HPR shock; year 1 denotes 2011, and year-0 estimates for 2010 are omitted because ASIF data are unavailable. Coefficients for pre-treatment years test for differential trends; the paper reports none for the main outcomes.

Heterogeneous effects by firm financial constraint are examined via triple-differences (p. 10):

$$y_{i,c,t} = \beta_1 \, \text{Treat}_c \times \text{Post}_t \times \text{FirmFC}_i + \beta_2 \, \text{Treat}_c \times \text{Post}_t + \beta_3 \, \text{FirmFC}_i \times \text{Post}_t + \lambda X_{i,t} + \theta X_{c,t} + \gamma_i + \gamma_t + \epsilon_{i,c,t} \tag{3}$$

where $\text{FirmFC}_i$ is a pre-reform financial constraint proxy (employment, total assets, property-holding status, or initial MRPK).

Capital misallocation is studied with an industry-city-year event study (p. 12):

$$y_{c,j,t} = \sum_{\tau=-4}^{3} \beta_\tau \, \text{Treat}_c \times I_t^\tau + \gamma_{c,j} + \delta_t + \epsilon_{c,j,t} \tag{4}$$

where $y_{c,j,t}$ is one of three cross-firm MRPK dispersion measures in 2-digit industry $j$, city $c$, year $t$: variance of log-MRPK, log-ratio of 90th to 10th percentiles, and log-ratio of 75th to 25th percentiles. City-industry and year fixed effects are included.

Aggregate TFP is computed via the Sraer and Thesmar (2023) aggregation formula (p. 12):

$$\Delta \log(\text{TFP}) \approx -\frac{\alpha}{2}\!\left(1 + \frac{\alpha\theta}{1-\theta}\right) K_s \Delta\Delta\hat{\sigma}^2 - \frac{\alpha}{2}\!\left(1 + \frac{\alpha\theta}{1-\theta}\right) \left[\Phi_s - K_s\right] \left(\Delta\Delta\hat{\mu} + \Delta\Delta\hat{\sigma}_{\text{MRPK},py}\right) + \frac{1}{2}\frac{\alpha\theta}{1-\theta} \Delta\Delta\hat{\sigma}^2 \tag{5}$$

where $\alpha = 1/3$ (capital share from Cobb-Douglas production), $\theta = 0.83$ (price elasticity of demand), $K_s = 0.5114$ (treated cities’ share of national capital), $\Phi_s = 0.4723$ (treated cities’ output share), and $\Delta\Delta\hat{\sigma}^2$, $\Delta\Delta\hat{\mu}$, $\Delta\Delta\hat{\sigma}_{\text{MRPK},py}$ are the estimated DiD effects on variance, mean, and covariance of log-MRPKs from Table 9 (p. 15).

Empirical specifications

Credit access (R1-R2). Eq. (1) uses firm leverage (debt/total assets) or interest rate (total interest/total debt) as the dependent variable in the ASIF panel, covering 261 cities over 2006-2013. Table 1’s full-sample columns 1 and 6 contain 1,845,072 and 1,761,814 observations; the controlled specifications used for R1-R2 (columns 2 and 7) contain 1,639,502 and 1,588,629 observations. City controls, firm controls, and pre-trend interactions are added in the controlled specifications. Results are also reported separately for private, state-owned, and foreign firms; private firms show the largest effects, consistent with greater financial constraints.

Real outcomes (R3). Eq. (1) applied to real firm outcomes: an indicator of positive non-housing investment, log non-housing investment, log non-housing capital, log industrial output, and log value-added (Table 2). The 427,088 to 655,568 firm-year observations used here are the subset of firms reporting investment data.

Crowding-out mechanism (R4-R5). Eq. (1) applied at the province-treatment-year level with log total mortgage loans (China Real Estate Yearbooks, 31 provinces, 35 major cities) and at the city-year level with log total LGFV loans (WIND database, 261 cities). Table 5 Panel A shows that HPR reduced housing transaction volumes and prices (cols. 1-4) and total mortgage loans (cols. 5-6). Panel B shows that HPR reduced primary land-market prices and areas sold by local governments (cols. 7-10), local government land-sales revenue (cols. 11-12), and total LGFV loan volumes (cols. 13-14).

For the city-level housing and local-government outcomes in Table 5, specifications include city and year fixed effects; city controls are added in alternating columns. Standard errors are clustered by city, except the household-mortgage regressions, which cluster at the province-treatment level. Samples contain 2,076-2,084 city-years for most outcomes, 1,937-1,947 city-years for primary land-market outcomes, and 351 province-treatment-years for household mortgage loans (Table 5, p. 8).

Bank constraints heterogeneity. Eq. (1) and a triple-differences variant separating cities by pre-reform bank leverage (above/below national median, Table 6, p. 10) and by bank deposits (Table 7). The HPR leverage effect is 8.62 pp for cities with high bank leverage vs. insignificant for low-leverage cities; the interest-rate effect is -2.91 pp vs. insignificant. Consistent with the crowding-out channel operating through bank balance-sheet constraints.

Tables 6 and 7 use firm-year leverage and interest-rate outcomes, firm and year fixed effects, city and firm controls, and pre-trend controls; standard errors are clustered by city. Table 6’s pooled triple-interaction estimates use 1,113,419 leverage and 1,084,545 interest-rate observations; Table 7 uses 1,638,916 and 1,588,046 observations respectively (Table 6, p. 10; Table 7, p. 14).

Firm constraints heterogeneity. Eq. (3) with four proxies for $\text{FirmFC}_i$ (Table 8, pp. 14-15). Firms with higher initial MRPK (2009) show a leverage interaction coefficient of 0.0084 (se 0.0035) and an interest-rate interaction coefficient of -0.0032 (se 0.0014), consistent with HPR differentially improving credit access for firms with higher initial MRPK.

Table 8 keeps firm and year fixed effects, city and firm controls, and pre-trend controls, with standard errors clustered by city. The row-specific samples range from 799,445 firm-years for the property-holding interest-rate specification to 1,393,754 for the asset-based leverage specification (Table 8, p. 15).

Capital misallocation (R6). Eq. (4) is applied at the 2-digit industry by city by year level. All three MRPK dispersion measures (variance, 90/10 ratio, 75/25 ratio) show significant relative reductions in treated cities after HPR, with no year-0 estimates because 2010 ASIF data are missing (Figure 5, p. 13). Corresponding MRPL estimates are smaller and less significant. Within-city capital misallocation contributes approximately 60% of overall MRPK dispersion in China.

Aggregate TFP (R7). Eq. (5) applied using parameter values from Table 9 (p. 15): $\Delta\Delta\hat{\sigma}^2 = -0.0998$, $\Delta\Delta\hat{\mu} = -0.0453$, $\Delta\Delta\hat{\sigma}_{\text{MRPK},py} = 0.0326$. The formula yields a 2.04% TFP improvement. A reduced-form approach directly estimating TFP at the 2-digit industry by city by year level finds treated cities improved TFP by approximately 6% relative to control cities; scaling by the 51% national capital share of treated cities gives 3.1%.

Datasets used

Dataset	Role in paper	Wiki page
Annual Survey of Industrial Firms (ASIF)	Main firm panel: leverage, interest rate, investment, output, MRPK; manufacturing firms with annual sales above 5 million RMB; approx. 1,845,072 firm-years	No page yet
WIND database (LGFV data)	Local government financing vehicle loan volumes for 261 cities, 2006-2013	No page yet
China Land Transaction Monitoring System (landchina.com)	Primary land market transaction prices and areas sold by city-year, 2006-2013	No page yet
CEIC China database	Secondary housing market transaction prices and sold housing area by city-year	No page yet
China Real Estate Yearbooks	Province-level and city-level home mortgage loan data, 31 provinces and 35 major cities	No page yet
National Tax Statistics Database (NTSD)	Firm-level property holding status and non-housing asset investments; approx. 700,000 firms annually	No page yet
China City Statistical Yearbooks	City-level covariates: GDP, credit/GDP ratio, real-estate investment share	No page yet

Sample: 261 Chinese cities, 2006-2013 (2010 excluded because ASIF data for 2010 were never released). The 45 HPR-treated cities implemented the policy in 2010-2011; 216 cities serve as controls.

When to read the full paper

Read the original to:

• Replicate the DiD estimates or extend to the post-2013 HPR relaxation period (after 2016).
• Examine the full battery of robustness checks: neighboring-city controls (Table 3), full ASIF sample (Table 4), balanced panel (Table A.5), alternative leverage and interest measures (Tables A.7-A.9), province-year fixed effects (Table A.10), and NTSD-supplemented estimates (Table A.11).
• Study the bank-constraints heterogeneity (Tables 6-7) and firm-level heterogeneity (Table 8, Figure 4) in detail.
• Apply the Sraer and Thesmar (2023) aggregation formula in other settings; parameters and derivation are in Table 9.
• Study the comparison with Basco et al. (2024) and Martín et al. (2021) on the relative importance of the crowding-out vs. collateral channel across countries.

Attribution and rights

Source: peer-reviewed, Journal of Banking and Finance 182 (2026), article 107584. Copyright 2025 Elsevier B.V. All rights reserved. This distillation was updated on 2026-10-04 and is not human-verified or independently reproduced. The source is paywalled; reproduction beyond brief extraction requires the publisher’s permission.

Liu, Yu, Peng Zhao, and Xiaoxue Zhao. “Housing booms and local capital misallocation.” Journal of Banking and Finance 182 (2026) 107584. DOI: 10.1016/j.jbankfin.2025.107584.