Caste, Faith, Gender: Determinants of Homeownership in Urban India

Abstract

Analyzing a large dataset of urban non-slum households, we find that homeownership tenure choice in India is significantly associated with gender, religion and caste. In particular, large households or those headed by women or with larger number of women are significantly more inclined towards homeownership than households of otherwise similar characteristics. Salaried households are the least and self-employed households the most likely to be homeowners. Compared to Hindus, Muslims show significantly lower while other minority religions (Sikhs, Buddhists and Jain combined) show significantly higher propensity towards homeownership after controlling for other factors. Castes which have been victims of discrimination show significantly higher propensity towards homeownership. The propensity towards homeownership in discriminated class households significantly increases when endowed with esteem or affluence.

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Notes

  1. 1.

    The constitution of India maintains “schedules” (lists) of specific castes and tribes which were identified to be extremely backward and are, therefore, known as scheduled caste (SC) and scheduled tribe (ST) respectively.

  2. 2.

    The ratio can be expressed in many ways: Price-to-Income, mortgage payments-to-income, total monthly expenses (including mortgage payment)-to-income, etc.

  3. 3.

    http://lawmin.nic.in/ncrwc/finalreport/v2b1-2ch9.htm

  4. 4.

    Schedule Article 366 (25)

  5. 5.

    Cairo, Manila and Beni Suef

  6. 6.

    https://thewire.in/33693/indias-urbanisation-is-dangerously-exclusionary-and-unequal/

  7. 7.

    According to Schedule 1 and Schedule 10 of NSSO 61st Round Survey, 2004–05

  8. 8.

    2001 census

  9. 9.

    http://timesofindia.indiatimes.com/india/OBCs-form-41-of-population-Survey/articleshow/2328117.cms

  10. 10.

    In the robustness check we replace the districts by state dummies as the price-rent ratios already control for the district level heterogeneity.

  11. 11.

    Nearly 7% of the homes in our sample were purchased readymade. The rest were constructed by the households.

  12. 12.

    http://hdfc.com/sites/default/files/HDFC_May05_15.pdf

  13. 13.

    We employ this specification in preference to estimating separate rent and price equations. We do this because our interest is limited to obtaining an estimate of a quality-controlled rent-price ratio for each city (as estimated by the city binary variables). If we had estimated separate equations we would have needed to specify an attribute vector for each city in order to obtain the average rent for the unit with those characteristics. The pooled specification allows us to bypass that somewhat arbitrary step.

  14. 14.

    Please see Appendix for more details.

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Acknowledgements

Authors are thankful to the anonymous referee of the journal for constructive feedback. Authors are also thankful to the following persons for their inputs or support: Piyush Tiwari, Zoltan Sapi, Jeremy Isnard, Venkatesh Panchapagesan, Ashwini Deshpande, Madalasa Venkataraman, Vivek Sah, Debarpita Roy, Divyanshu Sharma, Vinod Singh, Minu Agarwal, Gabrielle Bodenmann, Linnea Granberg, Participants of the IIMB CPP Conference (Bangalore, 2013), Participants of the AREUEA International Conference in Alicante, Spain (2016).

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Appendix

Appendix

Although the two-step method presented in the “Robustness Test” section, may provide an effective solution to work around the issue of data limitation, in our study it is limited to a smaller subsample of data for which the hedonic models could potentially lead to plausible QCRP ratio. However, the estimation of QCRP is limited to those districts for which some data is available both for rent and housing cost. In our analysis, this dataset reduces to 13,743 observations (from 44,883) and is limited to only 221 districts (out of 496) for which the hedonic model leads to valid QCRP ratios.

Appendix Table  6 compares the characteristics of the reduced sample with the larger sample. While income (EXP), faith and caste based breakup broadly remains the same, the two samples are substantially different from each other in terms of household composition and dwelling characteristics. In particular, the hedonic model is disproportionately dominated by renters (96% versus 34% in the larger sample), smaller household (3.8 versus 46), less female members (1.8 versus 2.4) and much less representation of female-headed households (0.7% versus 12%). This subsample is also characterized by smaller dwellings (348 sqft or 1.7 rooms versus 476 sqft or 2.2 rooms) closer to the workplaces with a larger representation of multistory units (37% versus 21%). Thus, inclusion of QCRP ratio may lead to biased coefficients primarily due to sampling issues. If the sampling bias is eliminated in the survey phase, this method could prove to be more effective.

Table 6 Comparison of tenure choice and pricing subsamples

Results from the combined OLS regression models for real estate cost (rent or cost of home) are shown in Appendix Table  7 . Since QCRP is a variable of interest, the final dataset for tenure choice has to exclude the districts for which the QCRP ratio is not available. Thus, the tenure-choice model has 30,717 household observations.

Table 7 OLS models of price (or Rent)

A large number of DISTRICT coefficients are statistically significant. Appendix Table  8 affirms the joint significance of incrementally introducing district dummies and the interaction term. Both the models are estimated with a sample of 13,743 household observations and have high adjusted R-squared values (82 to 83%).

Table 8 ANOVA for joint significance of district dummy variables

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Das, P., Coulson, N.E. & Ziobrowski, A. Caste, Faith, Gender: Determinants of Homeownership in Urban India. J Real Estate Finan Econ 59, 27–55 (2019). https://doi.org/10.1007/s11146-018-9672-1

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Keywords

  • Tenure choice
  • India
  • Caste
  • Religion
  • Gender
  • Housing

JEL Classification

  • R210
  • H8