Abstract
The immigrant population has grown dramatically in the US in the last 50 years. This study estimates housing demand among immigrants and discusses how immigration may be altering the structure of the US urban areas. Immigrants are found to consume less housing per capita than native-born US residents.
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Notes
Twenty-six of the forty largest cities as of 1950 were smaller in population by 1980. The number that declined over 1990–2020 fell to fourteen.
From 1950 to 2020 the proportion of the US population that is non-Hispanic White has fallen from 84 to 58%.
Any views expressed are those of the authors and not those of the U.S. Census Bureau. The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. This research was performed at a Federal Statistical Research Data Center under FSRDC Project Number 2481 (CBDRB-FY21-P2481-R9099).
The 1970 figures were taken from Greico et al. (2012). The 2019 statistics were taken from the American Community Survey, 2019.
The implicit prices retrieved from the log–log function are in levels. The hedonic price for characteristic zi: $${P}_{{z}_{i}}= {\alpha }_{i} f(z)\bullet \frac{1}{z}$$ where $${\alpha }_{i}$$ is the coefficient estimate for characteristic z, and the predicted value f(z) is not logged.
The average of the yearly Case-Shiller rates is used instead of the actual yearly statistics because the large appreciation rates calculated in years of the housing bubble (roughly 2003–2007) caused the implicit rents for much of the owned housing to turn negative.
A small complex is defined as 20 units or fewer which is at the 75th percentile of multiunit complexes, a large complex is defined as having more than 80 units which is at the 90th percentile.
The 2001 and 2003 AHS used 1990 census tracts. The assigning of 2000 census tract data was done through the use of Census overlay files. The 2011 AHS used 2000 census tracts and was transferred to 2010 by overlay files provided by the Census.
The 2001, 2003 and 2005 AHS exhibited high mismatch rates in which the place of birth for over 15% of the sampled immigrants could not be assigned to any country. These surveys were excluded from the models that included the place of birth variable.
The seven regional classifications are Central Asia, Latin America, Middle East, North America, East Asia, Africa, and South Asia. The World Bank designations are found in https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups. The only deviation this paper makes from the World Bank’s regional designations is for Australia and New Zealand. Those countries are included as part of Europe/Central Asia instead of East Asia.
Appendix 2 records the means and standard deviations of the variables used in the second stage. The first stage results are shown in “Appendix 1”.
This point was brought to my attention by a reviewer of this journal.
The Latin American and South Asian results are driven by the largest countries from the regions: Mexico and India. Fixed effects for immigrants specifically from those countries are larger (in absolute value) than the effects for countries’ respective regions. China, the largest country in East Asian, produces a point estimate half the size of its regional effect.
While the specifications control for income, the variation in housing demand across groups may partially reflect mean differences in wealth. The only non-housing wealth measure found in the data was a dichotomous variable indicating the size of household’s bank account in the 2001 and 2003 survey.
The immigrant housing deficit in percentage terms was calculated by dividing the immigrant fixed effect (129.155) in Table 3 by 1875.73, the square footage occupied per household estimated for Los Angeles. The 6.8% deviation is converted to the effect on population through the multiplier \(\frac{1}{1-.068}\).
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Acknowledgements
I wish to thank Zhen (Carol) Cui and the reviewers of this journal for helpful comments.
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Appendices
Appendix 1: First stage hedonic model
Variable | Full sample |
|---|---|
Intercept | 4.197** |
(0.943) | |
Housing characteristics | |
Square footage | 0.053** |
(0.013) | |
Bathrooms | 0.086** |
(0.032) | |
Bedrooms | 0.029 |
(0.027) | |
House age | − 0.023** |
(0.006) | |
Detached house | − 0.0003 |
(0.023) | |
Small complex | 0.285** |
(0.020) | |
Large complex | 0.326** |
(0.033) | |
Central air conditioning | 0.032* |
(0.019) | |
Urban characteristics | |
Median income | 0.532** |
(0.087) | |
Population | 0.040* |
(0.016) | |
Percent less than high school | 0.174* |
(0.077) | |
Percent college graduate | − 0.021 |
(0.091) | |
Percent Asian | 0.112** |
(0.030) | |
Percent Latino | − 0.012 |
(0.026) | |
Percent Black | − 0.037 |
(0.020) | |
Neighborhood characteristics | |
mean income | − 0.104** |
(0.037) | |
Percent less than high school | − 0.032 |
(0.025) | |
Percent college graduate | 0.270** |
(0.024) | |
Percent Asian | 0.064** |
(0.008) | |
Percent Latino | 0.030** |
(0.006) | |
Percent Black | − 0.002 |
(0.007) | |
Observations | 71,227 |
R2 | 0.1380 |
** and *Significance at 1% and 5% level.
Appendix 2:
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