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

Prashant DasAssistant Professor of Real Estate Finance

Ecole hôtelière de Lausanne, HES-SO // University of Applied Sciences WesternSwitzerland, Lausanne | Phone: (+41) 21 785 1623 | Email: prashant.das@ehl.ch

Alan ZiobrowskiProfessor of Real Estate

Georgia State University, 35 Broad St Suite 1404 Atlanta GA USA 3030Phone: (+1) 404 413 7726, Email: aziobrowski@gsu.edu

N. Edward CoulsonProfessor of Economics and Director, Lied Institute for Real Estate Studies

University of Nevada, Las Vegas Phone: (+1) 702 895-1660, Email: n.Ed.Coulson@unlv.edu

AbstractApplying multivariable probit models toon 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. Sikhs and Jains show significantly higher; but Muslims show significantly lower propensity towards homeownership after controlling for other factors. Castes which have been victims of discrimination show significantly higher propensity towards homeownership.

JEL Classification: R210 ● H8

Keywords: Tenure choice ● India ● caste ● religion ● housing

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

AbstractApplying multivariable probit models to on 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. Sikhs and Jains show significantly higher; but Muslims show significantly lower propensity towards homeownership after controlling for other factors. Castes which have been victims of discrimination show significantly higher propensity towards homeownership.

JEL Classification: R210, H8

Keywords: Tenure choice, India, caste, religion, housing

Introduction

Homeownership is an important policy goal. Aside from its potential role in improving life satisfaction (Dietz & Haurin, 2003; Rossi & Weber, 1996) and wealth creation—at least when house prices rise (Grinstein-Weiss et al. 2013; Gyourko & Sinai, 1999) homeownership potentially has positive externalities that creates benefits for society as a whole (Coulson & Li, 2013; DiPasquale & Glaeser, 1999).

Thus, the determinants of homeownership are of substantial interest; and these determinants have turned out to be manifold and complex. These determinants are, most importantly, considerations of household resources. Both income and at least some wealth are generally required to meet monthly mortgage obligations and down payment requirements (Boehm & Schlottmann, 2009; Hilber & Liu, 2008). Other household characteristics are important as well. Marital status, family size and gender, all play their roles, however much of the literature has focused on ethnic differences in homeownership rates, and whether those differences are due to differing socioeconomic circumstances, discrimination, immigration experiences or other demographic characteristics (Borjas, 2003; N E Coulson, 1999; Deng, Ross, & Wachter, 2003; Megbolugbe & Cho, 1996; Painter, Yang, & Yu, 2004).

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The ically literature on the housing tenure in less developed countries is thin. An early example was Malpezzi & Mayo (1987). Providing a survey of housing issues in such countries, Malpezzi, (1999) discusses the few studies of tenure choice and homeownership, and notes that such studies are difficult in part because of the paucity of survey data and data on the costs of renting versus owning. Fisher & Jaffe (2003) note that cross-country comparisons of homeownership are is fraught with difficulty, acknowledging the challenge that lies in providing “a single equation model with comprehensive explanation of worldwide homeownership rates.”

In this paper we address all of these issues in the context of the Indian housing market, which is to our knowledge the first such study. India is, of course, one of the most populous countries in the world, with both rising household incomes and an emerging middle class, making issues of tenure choice and homeownership an increasingly important subject. Proxenos (2003) notes that Indian homeownership is high, despite low household income and supply constraints that exist in many of its markets. India’s ethnic diversity is also very rich, and the interplay of caste and ethnic differences make examination of the role of demographic characteristics in tenure choice especially pertinent.

We examine the National Sample Survey of India’s Housing Conditions Survey data on almost 45,000 households and report several novel aspects of housing tenure choice. Contrary to expectations, we find the socially discriminated segments (scheduled castes and scheduled tribes) have a higher propensity to own homes after controlling for other determinants of homeownership. We show higher propensity among Sikhs and Jains but lower among Muslims compared to Hindus. Also, in line with the existing literature, we show significant evidence that homeownership propensity is enhanced by income, gender, occupation, employment and location.

The remainder of the paper is organized as follows. The next section discusses the background literature in the context of homeownership. In addition to known determinants of tenure choice, we discuss additional variables that are of interest in the present context. We next discuss our methods. A major empirical task, as discussed by Malpezzi (1999), is the construction of price data, and we outline our response to that. Following that, we discuss the results from our empirical models. Finally, we present conclusions of the study. We also discuss the limitations of our study and suggest topics for future research.

Background

Determinants of Homeownership

Carliner (1974) reports that stability of a household’s demand for housing, profile of the household-head, type of housing desired, household income and housing unit price are the main determinants of homeownership. Fundamental to the concept of homeownership is housing affordability (Bostic and Surette, 2001; Rappaport, 2008). However, the definition of housing

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affordability is somewhat varying. Although price-to-Income ratio1 is a widely accepted definition of housing purchase affordability, this definition has been criticized for its limitations in differentiating between high expenses versus low incomes (Fisher, Pollakowski and Zabel, 2009; Bogdon and Can, 1997; Lerman and Reeder, 1987; Linneman and Megbolugbe, 1992) and for its inflexibility in addressing the varying thresholds across the demographic characteristics (Nelson, 1994; Jewkes and Delgadillo, 2010). It may be argued that the affordability ratio could vary across the variables defining the demographic profile of households, such as social status. Yet, it is intuitive to believe that variables associated with household income and home prices will be significantly associated with the propensity to own homes. Coulson (1999) shows that homeownership rate falls with increase in the price of owner-occupied housing relative to rent. However, Hendershott (1997) and Capozza and Seguin (1996) show that high rent-price ratio are associated with peaks in housing prices. By inference, if high rent-price ratios reflect high rents then the ratio may have a positive association with homeownership. However, if the ratio reflects periods of low affordability, it may be negatively associated with homeownership rates.

Beyond housing prices, tenure choice has been affected by racial discrimination or ethnic differences. One plausible explanation for racially-based housing differences may be attributed to differences in “taste.” However, Kain & Quigley (1972) show than even after controlling for the factors that are known determinants of “taste;” there are significant racial differences in housing. Coulson (1999) reports significant difference in homeownership in the US based on household race. For example, black and Hispanic households were found to have lower propensity to own homes. The study shows that despite higher education and income levels, Asians exhibit lower homeownership rates. However, Painter, Yang, & Yu (2004) , based on their study of Los Angeles show that contrary to the immigration-literature expectations, Chinese homeownership rates are substantially higher due to the cultural influence of “home owning peers.” Gabriel & Painter (2008) show significantly different homeownership patterns across white and black communities.

Caste and Homeownership

While ethnic differences in homeownership attainment have been shown to be important in the US, little has been done on this topic outside the US. As noted above, India is an excellent test case for this phenomenon in part due to the multicultural nature of its population and the still pervasive influence of the caste system. Significant association between castes and homeownership may suggest policy-level intervention. The nature of such policy interventions has been debated Ito (2009) argues that India’s reservation policies (which are akin to affirmative action policies in the US) have had very limited impact on caste-based discrimination. Chin & Prakash (2011) show that reservation policy reduces poverty in the scheduled tribes (ST) group, but not in scheduled castes (SC). However, some other studies (such as Bertrand, Hanna, & Mullainathan, 2010) report that caste-based reservation policies can be a successful tool in both increasing diversity and allocating resources to relatively

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

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disadvantaged families. Nevertheless, our understanding of caste-based differences in housing tenure propensity should help in improving policy measure to address the differences appropriately.

Akerlof (1980) shows that although breaking away from social customs may offer improved employment-related utility for individuals, such customs may still be followed by “a stable fraction of the population” which believes in these customs. Boehm & Schlottmann (1999) finds “the housing tenure of parents plays a primary role in determining whether or not the child becomes a homeowner.” Such intergenerational momentum in housing tenure choice may further enhance caste-based propensity towards tenure choice given such disparities on the fronts of income and education. Although caste-concentration may be location-specific, for reasons mentioned above, caste could still be a significant determinant of tenure choice. For example Mazzocco & Saini (2012) argues that the relevant unit in rural India is caste, not the location. One may infer from this finding that despite economic prosperity, the caste identity may have significant impact on a household’s income (or expenditure, thereof). Luke & Munshi (2007) report that in their sample, wages did not vary by caste. However, Ito (2009) further reports that people from lower costs face more difficult entry into the labor market rather than wage-discrimination.

Studies have also found that the caste system has a significant impact on a person’s socio-economic attitude (Singh, Gaurav, & Ranganathan, 2012) and access to resources (Ito, 2009) although none of these studies examines the question of whether the caste is associated with tenure choice. Munshi & Rosenzweig (2009) show that households of higher-income castes are less likely to migrate and marry beyond caste; and that there is substantial risk-sharing within a community which may restrict mobility. This is of interest because restricted mobility has been associated with homeownership (Coulson and Fisher, 2009). Bostic & Surette (2001) show that family-related characteristics (such as race and income) explain homeownership trends only in relatively higher income groups. Therefore, one should expect higher homeownership rate among people of “forward” caste.

However, Luke & Munshi (2007) report that “lower” caste households spend more on their children’s health than “upper” caste households despite the same income and access to the same facilities. On a similar note, Green & White (1997) measure high monetary benefits for low-income households to turn from renters to homeowners. Therefore, if homeownership is considered as a means of improving family welfare, such households should show strong preference for homeownership. The phenomenon is explained by higher return on human capital enjoyed by lower castes compared to their ancestral places. To summarize, the literature does not offer a definitive expectation regarding the association between castes and housing tenure choice.

Gender

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Carliner (1974) shows that homeownership rate in families with a female household head is higher compared to other households. Malpezzi & Mayo (1987) show mixed evidence regarding the impact of female household on housing demand across three cities (Cairo, Manila and Beni Suef). Gender-based differences have also been documented in several other socio-economic contexts. Madden (1987) shows significant difference of earnings between women and men in the US primarily due to gender-based discrimination. Besides, gender is found to affect how individuals choose to allocate assets in retirement plan (Sundén & Surette, 1998). Although weak, Riley & Chow (1992) offer weak evidence that women are more risk-averse than men in their sample. Luke and Munshi (2007) report: “a relative increase in female income weakens the family's ties to the ancestral community and the traditional economy.” Elderly women tend to have lower levels of financial assets and therefore are found to be more risk-averse in the capacity of household-head (Megbolugbe, Sa-Aadu, & Shilling, 1999). Besides, caste and gender have distinct impact on economic decisions such as expenditure and policy decisions at collective levels (Clots-Figueras, 2011). For example, Clots-Figueras (2011) shows that female legislators from lower castes or disadvantaged tribes invest more heavily in health and early education compared to female legislators from upper castes.

Religion

A number of studies have focused on the association between religious affiliation and economic behavior. For example, Stulz & Williamson (2003) show cross-sectional difference in creditor rights between catholic and protestant countries. Some studies show significant association between overall “religiosity” as well as cross-faith ratios and risk-aversion (Kumar, Page, & Spalt, 2011; Hilary & Hui, 2009). In a widely cited study by Guiso, Sapienza, & Zingales (2003), it has been shown that after controlling for country fixed-effects, religious beliefs are associated with socio-economic behavior. But little has been done on the relationship between religion and homeownership.

So far, we find no empirical study that focuses on the determination of housing tenure choice in India applied to a large sample and appreciating a complex set of determinant variables 2. A number of existing studies offer some potential candidate variables for tenure choice determination. However, caste and religion based classifications in India exist in their unique settings. While studies have offered some insights on the association of these specific variables on other socio-aspects, they do not address the question of tenure choice as yet. The synthesis of the literature, however, provides useful background to develop our empirical models.

Data

Demographic Profile of India

According to the 2011 census, India’s population is 1.21 billion, of which nearly 31% resides in urban areas. During 2001 and 2011, the average annual population growth rate in both the male

2 Tiwari & Parikh (2011) and Tiwari & Parikh (1998) provide housing demand models for India and Mumbai.

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and female population was 1.2%. Overall, the female-to-male ratio stood at 0.943 nationally and nearly 0.930 in urban areas. The average household size in India3 is 4.91, which reduces to 4.78 in urban areas. Thus, average number of male household members in the country and urban areas stand at 2.53 and 2.48 respectively.

The legal system of India acknowledges how certain segments of the society were “neglected, marginalized and exploited” for centuries4. Provisions were put in place to address the issues of untouchability, discrimination and prejudice against these segments. To objectively identify these segments across the states and territories, the constitution of India prescribes certain “schedules” in which specific castes and tribes5 from various states have been listed. These most neglected communities are called scheduled castes (SC) or scheduled tribes (ST). The constitution also identified some relatively less marginalized but yet backward communities as “Other Backward Castes” (OBC). The rest of the population is considered to be “Forward” caste. In colloquial language the residual segments is also referred to as “General” caste. In urban areas, scheduled castes and scheduled tribes represent nearly 13% and 3% respectively (which is considerably less than the national averages of 18.5% and 11%). Anecdotally estimated, people belonging the OBC group represent nearly 40% of the population6. 80% of the national population is Hindu and 13% is Muslim. Christians, Sikhs, Buddhists and Jains represent respectively 2.3%, 1.9%, 0.8% and 0.4% of the national population. According to the Indian constitution, caste and religion are two independent identities. For example, a person could be “Forward caste-Muslim”, “OBC-Jain” or “SC-Hindu” and so on.

Overall, 87% of households in India are homeowners. The ratio is slightly high among SC and ST segments (90% and 91% respectively). The homeownership rates considerably shrink in urban areas to 69% (SC: 74%, ST: 65%). In particular, the homeownership shrinkage7 caused by urban-rural disparity is slightly less severe in scheduled castes (0.77) compared to scheduled tribes (0.68) and the overall population (0.69).

Table 1 provides the definition of variables used in this study. The data is extracted from the 65th round (2008-09) of “Housing Conditions Survey” (HCS) conducted by National Sample Survey (NSS) Organization of India. The database of 153,518 households across India details household characteristics (size, gender-mix, homeownership status, consumer expenditure, caste, religion, etc.), household head characteristics (profession, occupation, gender, etc.), location characteristics (state, city, population, slum-categorization, etc.) and dwelling characteristics (floor area, single-family or multistory, etc.) among other variables. The data includes 56, 374 urban households. From urban households we exclude those categorized as ‘slum’ category or those having missing data. Observations with monthly housing rent less than INR 100 (nearly US$1.5) and home values less than INR 50000 (nearly US$ 750) are also excluded from our

3 2001 census4 http://lawmin.nic.in/ncrwc/finalreport/v2b1-2ch9.htm5 Schedule Article 366 (25)6 http://timesofindia.indiatimes.com/india/OBCs-form-41-of-population-Survey/articleshow/2328117.cms7 Homeownership rate in urban area / homeownership rate in rural area.

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sample leading to 44,883 observations spread across 507 urban districts. Of these, housing rent or value data is available for 13,743 households.

Insert Table 1 here

Table 2 provides the summary of variables used in this study. Means of the dummy variables signify their representation (%) in the sample. For example, our sample of non-slum urban households includes 12% households with their heads working as managers, 6% as technicians, 5% as clerks, 15% as shopkeepers, and so on. Urban renter households have 12% excess representation in our sample compared to national average (25% versus 13%). Nearly 37% of the household heads are salaried, 39% are self-employed and 13% work as casual laborers. The average household size is 4.58 (census = 4.78) with 2.35 males per household (census = 2.48). Representations of SC (13%) ST (8%) and OBC (37%) in our sample are broadly aligned with the national urban averages. Compared to national urban average, Hindus and Sikhs are slightly underrepresented while Jains, Buddhists, Christians and Muslims are slightly over-represented. However, the difference from the national averages are small (0 – 4.5%). Thus, our sample is a fair representation of the national population. Housing units in our sample offer a wide range of characteristics with average floor area of 476 sq. ft. having 2.16 living rooms. 72% homes have access to tap water, 96% have electricity, 70% can be reached from a motorable road and 73% enjoy streetlight.

Insert Table 2 here

Before conducting our mulitvariable analysis, we compare households across their housing tenure (renters versus owners) in Table 3 using sample averages of salient variables. Average monthly rent is 1,228 (ranging between 100 to 30,000) which appears realistic₹ ₹ ₹ 8. The cost of owned homes has an average of 283,088 (ranging between 50,000 to 2,600,000). These₹ ₹ ₹ costs reflect the price paid for a ready-made unit or the cost of new construction (including land price). Only recent purchases or construction are included in the data to control for the effect of time on real estate valuation. Given that our sample focuses on urban areas and excludes urban slums, the cost of owned homes appears severely under-reported. This is evident from the fact that according to HDFC9, a leading housing finance company in India reported the average home prices to be around 3,000,000 during 2008-2009. Understating of wealth in India is a well-₹documented phenomenon (Zacharias & Vakulabharanam, 2011) which biases statistical estimates. Underreporting of home prices, in particular, is attributed to high stamp-duty (tax to be paid during the transfer of property title) varying with local governments in India. Therefore, we assume that the extent of understatement is location specific. Therefore controlling for location in the hedonic model for pricing could offer an effective tool to examine the determinants of pricing so long as our objective is to draw comparisons.

Insert Table 3 here

8 denotes Indian rupees. Recently, 1 US$ ≈ 60 to 65₹ ₹ ₹9 http://hdfc.com/sites/default/files/HDFC_May05_15.pdf

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Nevertheless, Table 3 provides a useful preliminary profile of household tenure-choice. Average household monthly expenditure (which is considered to be directly associated with income) is higher among owners. Besides, renters prefer localities that allow superior access to the household head’s place of work. This is congruent with earlier studies that associate homeownership with restricted mobility (e.g. Coulson & Fisher, 2009). The pProportion of households where the head does not have to travel to work is higher among renters (37%) than owners (34%). Similarly, the proportion of households who have to travel more than 15 kilometers for work is substantially smaller among renters (6%) than owners (13%). While more than 70% renters have access to motorable roads and streetlight, s, the ratio is nearly 60% for owners. Although renters, on average, appear to enjoy superior access to workplace compared to homeowners, they often live in dwellings with inferior features. Average rented homes tend to be smaller (332 sq.ft. versus 705 sq.ft.; 2 bedrooms versus 3 bedrooms) compared to owner-occupied homes. The Pproportion of rental homes in multistory buildings is substantially high among renters (38%) compared to homeowners (13%) reflecting the fact that renters tend to live in dense urban areas close to workplace. Also, compared to owned homes, fewer rented home enjoy good-quality ventilation (42% versus 64%), bathrooms attached to the dwelling units (48% versus 64%) or exclusive latrines (52% versus 85%). Although the observations made above provide the comparison across renter and homeowner households, we need to control for confounding variables to draw statistically significant inferences.

Methodology

Our empirical methodology includes two steps. In the first step, we model the dwelling price (rent, or cost) using a hedonic model that includes location fixed effects controlling for locations at the district level. The coefficients of these variables are utilized to calculate the quality-controlled rent-price (QCRP) ratio for each district. With other variables, the QRCP ratio is used as a determinant of the binomial tenure choice model examined in the second step.

Step -1The cost of dwelling (RECOST = total cost of acquiring an owned home or the monthly rent of a rented home) is modeled with typical hedonic variables such as dwelling characteristics (e.g. floor area), locality characteristics (e.g. street light), location (district) and tenure (owned versus rented) status as shown below:

ln ( RECOST )=A+B 1. [ dwelling characteristics ]+B 2. [ locality characteristics ]+B 3.DISTRICT +B 4. RENTED(EQ.1)

eB 4 is estimated discount factor across all cities which adjusts for the cost differential between homeownership and rental. However, geographic dissimilarity of real estate markets would suggest that the discount factor could vary across districts. To control for such variations, we introduce an interaction between the district and the tenure choice of a household (i.e. DISTRICT*RENTED):

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ln ( RECOST )=A+B 1. [ dwelling characteristics ]+B 2. [ locality characteristics ]+B 3.DISTRICT +B 4. RENTED+B 5. DISTRICT∗RENTED(EQ.2)

As shown below (EQ. 3 to EQ.5), fitted value for rent (EQ.4) divided by the fitted value for dwelling cost (EQ.3) is the expected rent-price ratio for each city.  Controlling for dwelling and location characteristics, variations in the expected value for Ln(RECOST) is B4, and that for Ln(RENT) is B3+B4+B5.  Therefore, the rent-price ratio for each city calculated as Ln(RENT/COST) equals B4+B5.  We term this ratio the quality-controlled rent-price (QCRP) ratio. An expectation is that B4 is negative so that the recurring monthly rental cost is always lower than the one-time cost of ownership. It can be shown that a positive B5 signifies shrinkage in the cost differential between owning and renting (i.e. the rent/price ratio increases).

From EQ.2; for a given district (i.e. DISTRICT = 1); if RENTED = 0,

ln (COST )=A+B 1. [ dwelling characteristics ]+B 2. [ locality characteristics ]+B 3∗1(EQ.3)

Also, for a given district (i.e. DISTRICT = 1); if RENTED = 1,

ln ( RENT )=A+B 1. [dwelling characteristics ]+B 2. [location characteristics ]+B 3∗1+B 4∗1+B 5∗1∗1(EQ.4)

Subtracting EQ.4 from EQ.3 (i.e. EQ.3 – EQ.4) implies that for a given district,

Ln(RENT)-Ln(COST) = B 4+B 5 (EQ.5)

5)

It logically follows that expensive rental dwellings will encourage the choice of ownership. If so,We one may hypothesize that the QCRP ratio would have a positive coefficient in the ownership tenure-choice model. However, such ratios have an inherent limitation. The hypothesis may only be tested in an environment where home prices are considered stable across the sample; or when the cost of ownership is considered affordable across the sample. Take an observation with high QRCP ratio, for example. One cannot confidently attribute it to higher than expected numerator (RENT) or lower-than-expected denominator (COST). Studies such as Fisher, Pollakowski, & Zabel (2009) and Bogdon & Can (1997) point out such a limitation in housing price-to-household income ratio. Besides, high QRCP ratio may reflect already high prices, potentially associated to investment demand in housing: wealthier households who would like to capitalize on high current-yield potential offered by high QCRP may quickly drive the home-prices up, making them unaffordable for less-wealthy potential buyers. This argument is consistent with Hendershott (1997) and Capozza and Seguin (1996) who report that high rent/price ratios indicate lower future appreciation, a finding which is consistent with peaking prices. The overall nature of association between the QCRP ratio and tenure choice, therefore, is a question of empirical inquiry specific to the context.

Step-2

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In the next step, we analyze binary (probit) models of homeownership. Since household income data is not available, we use expenditure (EXP) and a set of known determinants of income as a proxy for income. Variables such as profession of the household head and the nature of employment determine the household income10. We test this hypothesis by regressing the consumption with variables that are logical determinants of income level (such as the profession, employment type and location controls) of the household head. The results are available11 in Appendix Table1. The first tenure choice model is specified as follows:TenureChoiceOwn=1 ,rent=0= f (QCRP , exp , District dummy) (EQ.6)

The district dummy and the QCRP ratio control for prevalent housing price trends. If the consumer expenditure is related to household income, then the regressors offer sufficient information relevant to determining home-price affordability. However, the model (EQ.6) described above is underspecified in light of the arguments we build based on our literature synthesis. In particular, we argue that beyond the macroeconomic trends and consumer expenditure, the household-specific characteristics play an important role. In particular the role of variables such as gender, profession, caste and religious affiliation may have significant effect on the propensity to own homes. Therefore, the following models are specified with sets of variables added incrementally to the model:Tenure ChoiceOwn=1 ,rent=0=¿ f(QCRP,EXP,State dummy, Expenditure deteminants, Household

demographic characteristics, Household religious affiliation, Household caste) (EQ.7)

Results and Discussion

Results from the combined OLS regression models for real estate cost (rent or cost of home) are shown in Table 4. Model (1) which is based on EQ.1 includes tenure choice, dwelling characteristics, and specific characteristics of the locality where a dwelling is located. The model controls for 457 Districts included in the data. Model (2) also introduces the interaction term between the dummy variable for RENTED and 457 district dummies. A large number of DISTRICT coefficients are statistically significant. For example, in Model (2) 125 DISTRICT coefficients and 86 RENTED*DISTRICT coefficients are significant12. Coefficients for DISTRICT and RENTED*DISTRICT are not shown in the table for brevity. Both the models are run on a sample of 13,743 household observations and have high adjusted R-squared values (82% to 83%). For each district, the sum of the coefficients of DISTRICT and

10 We test this hypothesis by regressing consumption on variables that are logical determinants of income level (such as the profession, employment type and location controls) of the household head. The results are available in Appendix Table1.

11 We develop the series of corresponding income (INC) by applying a 30 percent income tax rate and the annual propensity to save(APS) estimates based on Burgohain (2009). In particular, the consumption levels included in our study, the total consumption is nearly 65 percent of the total income12 Most of these coefficients are significant at 0.1% level. However, for brevity they are not reported individually in these tables.

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RENTED*DISTRICT serves as the QCRP ratio. However, due to sampling-related issues, from Model (2), we can only observe the QRCP variable for 221 districts. Since this variable is a determinant of the tenure-choice in our model, 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.

The tenure-choice related coefficients in these models (and hence, the QCRP ratio) is prone to bias due to the issue of understated housing cost data. Yet, so long as the location is controlled for and the underreporting bias is systemic in a location, the sign, significance and mutual comparison of these coefficients should be statistically valid. As expected, RENTED has a significantly negative coefficient. Homes which are purchased are substantially13 more expensive compared to self-constructed homes. However, this comparison may be confounded when most ready-made homes are situated in multistory condominiums in specific localities of a city. Homes rented from employers have significantly less rents as expected.

Insert Table 4 here

Adding a bed room increases the real estate cost (price or rent) by 13%. Similarly, the number of other rooms, floor area and covered verandah increase the real estate cost. Availability of tap water is insignificant. However, units with availability of exclusive bathrooms (attached or detached from the main dwelling) enjoy significant premium. The difference of premium across attached and detached bathrooms, however, is minor. Access to exclusive latrine leads to 30% premium. Although access to (only) public latrines has a negative coefficient, it is insignificant. Pucca roof (roofing made with brick-concrete or superior quality materials) and good-quality flooring (e.g. terrazzo, marble, etc.) enjoy 12%-14% premium each. Units in multistory buildings cost nearly 10% more. Good ventilation enjoys up to 11% premium whereas bad ventilation conditions experience 5% discount. Similarly, dwellings identified to be in “good-condition” enjoy 7-8% premium whereas the discount on bad-condition dwellings is severe (14%). Similarly, good drainage and garbage disposal systems at the locality level are rewarded. Access to electricity leads to over 30% of premium. Localities with streetlight add 3% premium to real estate costs.

However, travel time to work has counter-intuitive coefficients. One would expect that homes closer to workplaces are more expensive. Households who live 15 kilometers or more away from workplaces pay nearly 16% higher real estate cost compare to those who do not have to travel for work at all. Selection bias is a possible explanation: households who stay close to work-places have to accept substantially inferior quality dwellings compared to those who stay away from the central business districts, but are willing to pay more for better-quality dwellings. We find that falling in a flood-prone area has an insignificant effect on real estate costs. However, this may be due to the fact that floods are specific to localities. Certain districts will have more flood-prone areas than others. Thus, the location dummy potentially captures some variation caused by flood-proneness.

13 e1.027≈ 2.8 ;e1.091≈ 2.98 ;

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Table 5 shows the results of various tenure choice models. All models are based on the tenure-choice data of 30,717 households spread across 221 districts and 34 states or union territories. Since the QCRP ratios will be perfectly collinear with the district dummies, these models control for States rather than districts. We also report pseudo R squared measures. Despite their limitations pseudo R-squared, offer an effective way to compare probit models with each other.

Insert Table 5 here

Model (1) is the fundamental model with elements of housing affordability: consumer expenditure and QRCP ratio, in particular. Model (2) replaces consumer expenditure data with the determinants of household income. Model (3) adds household characteristics. Models (4) and (5) incrementally add household religion and caste information. Model (6) includes all available variables. Including the determinants of income (Model (2)) substantially improves the model efficiency. Similarly, including household characteristics also improves the model. The incremental effect of adding caste and religion variables does not substantially improve the model. Our literature survey identifies several studies that relate caste and religion variables to be associated with income. Thus, including the determinants of income and these variables together may not improve the models substantially. However, the individual association of these variables with the tenure status may still be observed.

From the two models, we find that the QCRP ratio is negative and statistically significant. This confirms the peaking-prices phenomenon described in Hendershott (1997) and Capozza and Seguin (1996). High QCRP may reflect pricing bubble and discourage homeownership. Consumer expenditure (which is associated with income) is positively associated with a household’s propensity to own homes as expected. Gender has a significant impact on tenure choice. Households headed by male members, or having more male members (or of large size, in general) have lower propensity to own homes. This confirms the findings of Carliner (1974). Traditionally, investing in homes is considered to be an investment in future and a risk-averse strategy. Thus, this finding is consistent with Madden (1997), Sunden and Surette (1998), Riley & Chow (1992), Luke and Munshi (2007) and Megbolugbe et. al (1999). From an occupational standpoint, skilled labor improves homeownership whereas elementary work skills lead to the lowest propensity to own homes. Salaried household heads have the lowest propensity to be homeowners. Self-employed household heads are associated with substantially higher propensity to be homeowners followed by casual labor or other types of employment.

After controlling for other factors such as location, household characteristics, occupation and employment status of the household head, Muslim and Buddhist households have a significantly lower propensity to be homeowners compared to Hindu households, although the difference is statistically insignificant among Buddhist households. Also, Sikh households have the highest propensity to own homes followed by Jains. Christian have a slightly higher propensity to own compared to Hindus, although statistically insignificant. These studies contribute to the evolving literature on socio-economic impact of religious faith (Stulz & Williamson, 2003; Kumar, Page & Spalt, 2011; Hillary and Hui, 2009; Guiso, Sapienza & Zingales, 2003).

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Our findings on the impact of caste on homeownership rates are strikingly different from what one would expect based on the literature on discrimination (e.g. Kain & Quigley, 1972; Coulson, 1999; Ito, 2009). For example, Coulson (1999) shows a negative association between discrimination and homeownership in the US Our findings suggest that in India compared to “forward” castes, households from the scheduled castes and scheduled tribes (“discriminated classes”) have significantly higher propensity to own homes, after controlling for other factors. Difference between the forward caste and “other backward classes” category is inconclusive and insignificant. Comparing the national average ownership to urban ownership of homes across castes shows that although shrinkage in homeownership is universal in urban areas (See Appendix Table 2), it is the least severe in scheduled tribes and the highest in scheduled castes14. The net effect, however, is that these under-privileged castes retain their higher propensity to homeownership in urban areas without showing any tendency to break away from their caste-based affiliation as would be predicted by Akerlof (1980) or Ito (2009).

Conclusions

Despite some debate recently, findings from most studies on homeownership unequivocally support the notion that homeownership is a preferred tenure choice from micro as well as macroeconomic perspectives. The question of what causes a household to become a homeowner versus a renter has been widely studied. However, due to lack of data, such studies have primarily focused on developed markets. Emerging markets offer an effective laboratory, given their distinct socio-cultural pattern, to answer novel research questions regarding the association between homeownership and specific social aspects. In this study we capitalize on a rich national sample survey database of housing conditions conducted by the government of India during 2008-2009. Focusing on urban, non-slum India, first we develop rent-price ratio at district levels using combined hedonic models for rent and housing cost (price). This ratio, along with other household and locality-level variables is utilized in our binary probit model of homeownership choice. We find that determinants of household income are positively associated with a household’s propensity towards homeownership. Besides, high monthly consumer expenditure of a household is associated with improved propensity to own home. We find that there are statistically significant differences in the overall homeownership propensity across states and across different districts within a state. A household headed by females, or with larger number of females shows a higher propensity to homeownership. Similarly, larger households have a substantial positive association with homeownership. Salaried people are least inclined towards homeownership compared to all other employment types. We find that while Sikhs and Jains are more inclined towards homeownership, Muslims show smaller propensity compared to Hindus after controlling for other factors. More importantly, although “forward” caste and other backward caste (OBC) communities show no statistical difference between each other, people in scheduled castes and scheduled tribes have significantly higher propensity towards homeownership.

14 data specific to other backward castes (OBC) is not available

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Our findings, however, must be interpreted further with extreme caution. First, additional known determinants of homeownership rate such as household income are not available from the Housing Conditions Survey data. Despite available accurate information on proxies for income, actual income data may further enhance the results. A large portion of caste and religious dynamics may also be captured in location controls (states, districts, etc.). Thus, our dummies for caste and religion only signify marginal associations between them and homeownership above and beyond how they are associated with it differently in each state. For example, consider a particular location (state or district) where SC communities have very low propensity towards homeownership. Despite the SC dummy having a positive coefficient, the net effect could be that the SC people in this location have lower propensity towards homeownership rates. Policy interventions must appreciate such variations. Also, it must be understood that while a household may be a homeowner, the home it owns may be less than adequate. Nevertheless, our study offers sufficient evidence that caste and religion are significantly associated with a household’s tenure choice.

Our study focuses on urban India only. Future studies could also examine rural India. Studies have shown significant cross-variable effects on homeownership rates. Interactions of age, gender, religion and caste may offer further insights on the determinants of homeownership. House-price appreciation, and costs of owning may vary over time and in cross-section (Boehm & Schlottmann, 2011). With new data evolving about the Indian markets in these areas, the study could be extended to include these variables. Due to lack of data, this study does not distinguish between a household’s continuous homeownership versus transition in tenure choice from renting to owning and vice versa (Boehm & Schlottmann, 2009, 2011), Communities may have varying tendencies to tenure choice in these situations which could be a topic of further analysis.

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Table 1. Variable Descriptions

Occupation of the Household Head (Dummy Variables, reference group = “Professionals”)MANAGER Legislator, Senior Official or ManagerTECHNICIAN Technician or Associate ProfessionalCLERK ClerkSHOPWORK Service Worker or Shop & Market Sales WorkerSKLABOR Skilled Agricultural and Fishery WorkerCRAFTSMAN Craft and Related Trades WorkerOPERATOR Plant and Machine Operators and AssemblerELEMENT Elementary OccupationEmployment Type of the Household Head (Dummy variables, reference group = “Salaried”)SELFEMP Self-EmployedCASLAB Casual LaborOTHEREMP OthersDemographicsMALENO Number of male members in the householdHHOLDSIZE Total number of members in the householdHEADMALE Equals 1 if the household head is a male; 0 otherwise

EXP Household consumer expenditure (in Indian Rupees) during the last 30 days, excluding housing (rent or purchase)-related expenses

Religion of the Household (Dummy variables, Reference group = “Hindu”)MUSLIM Equals 1 if Muslim, 0 otherwiseCHRIST Equals 1 if Christian 0 otherwiseSIKH Equals 1 if Sikh 0 otherwiseJAIN Equals 1 if Jain 0 otherwiseBUDH Equals 1 if Budhhist, 0 otherwiseHousehold Caste (Dummy variables, reference group =”General Caste”)SC Equals 1 if "Scheduled Caste", 0 otherwiseST Equals 1 if "Scheduled Tribe". 0 otherwiseOBC Equals 1 if =Other Backward Castes", 0 otherwiseDwelling CharacteristicsRENTED Equals 1 if the household is a renter, 0 otherwise

PURCHASED Equals 1 if the household is homeowner and the home was purchased as readymade, 0 otherwise

QUARTER Equals 1 if the household is renter and lives in employer-provided quarter, 0 otherwiseFLOORAREA Floor area of the dwelling (sq.ft.)LIVINGROOMS Number of BedroomsOTHERROOMS Number of other roomsCOVVERAND Equals 1 if the dwelling has a covered verandah, 0 otherwiseUNCOVVERAND Equals 1 if the dwelling has an uncovered verandah, 0 otherwiseTAPWATER Equals 1 if the dwelling has access to tap water, 0 otherwise

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ATTBATH Equals 1 if at least 1 bathroom has a direct access from rooms, veranda or corridor, 0 otherwise

DETBATH Equals 1 if the bathroom is in a structure separated from the main building , 0 otherwiseEXCLATRINE* Equals 1 if the latrine facility is for the exclusive use of the household, 0 otherwisePUBLATRINE* Equals 1 if only public latrine facilities are available in the locality, 0 otherwisePUCCAROOF Equals 1 if the roof is made of brick concrete or is of superior quality, 0 otherwiseCEMENTFLOOR Equals 1 if the floor is finished with cement, 0 otherwiseGOODFLOOR Equals 1 if the floor is finished with tiles or mosaic, 0 otherwiseMULTISTORY Equals 1 if the building has more than one floors, 0 otherwiseGOODVENT** Equals 1 if the ventilation quality is assessed as "good", 0 otherwiseBADVENT** Equals 1 if the ventilation quality is assessed as "bad", 0 otherwiseGOODDRAIN** Equals 1 if the drainage quality is assessed as "good", 0 otherwiseBADDRAIN** Equals 1 if the drainage quality is assessed as "bad" 0 otherwiseGOODGARBAGE Equals 1 if the ventilation quality is assessed as "good", 0 otherwiseBADGARBAGE Equals 1 if no arrangements for garbage collection is noted, 0 otherwiseGOODCOND** Equals 1 if the structure is found in "good" condition, 0 otherwiseBADCOND** Equals 1 if the structure is found in "bad" condition, 0 otherwiseT5KM Equals 1 if the maximum distance to the place of work is up to 5 kilometers, 0 otherwise

T15KM Equals 1 if the maximum distance to the place of work is between 5 to 15 kilometers, 0 otherwise

T15PLUSKM Equals 1 if the maximum distance to the place of work is beyond 15 kilometers, 0 otherwise

FLOOD Equals 1 if the dwelling experienced any flood (rain/river/sea) in last 5 years, 0 otherwiseSTREETLIGHT Equals 1 if the approach road has street light, 0 otherwiseELECTRICITY Equals 1 if the electricity for domestic use is available, 0 otherwiseMOTORABLE Equals 1 if the approach road is motorable, 0 otherwise

Notes: * dwellings which do not have access to latrine at all are excluded from the sample. The reference group includes dwellings that used latrine shared with several households. **the reference group is "satisfactory" quality

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Table 2. Descriptive StatisticsMean St. Dev. Min Max

MANAGER 0.12 0.33 0 1TECHNICIAN 0.06 0.24 0 1CLERK 0.05 0.23 0 1SHOPWORK 0.15 0.36 0 1SKLABOR 0.05 0.21 0 1CRAFTSMAN 0.14 0.35 0 1OPERATOR 0.07 0.26 0 1ELEMENT 0.16 0.37 0 1SELFEMP 0.39 0.49 0 1CASLAB 0.13 0.34 0 1OTHEREMP 0.11 0.31 0 1MALENO 2.35 1.43 0 19HHOLDSIZE 4.58 2.47 1 36HEADMALE 0.88 0.33 0 1EXP 5,456 4,331 100 1,50,000MUSLIM 0.14 0.34 0 1CHRIST 0.07 0.25 0 1SIKH 0.02 0.13 0 1JAIN 0.01 0.08 0 1BUDH 0.01 0.08 0 1SC 0.13 0.33 0 1ST 0.08 0.26 0 1OBC 0.37 0.48 0 1RENTED 0.25 0.43 0 1QUARTER 0.07 0.25 0 1FLOORAREA 476 362 10 6,300LIVINGROOMS 2.16 1.28 0 17OTHERROOMS 1.61 1.26 0 14COVVERAND 0.27 0.44 0 1UNCOVVERAND 0.24 0.43 0 1TAPWATER 0.72 0.45 0 1ATTBATH 0.49 0.50 0 1DETBATH 0.36 0.48 0 1EXCLUSIVELATRINE 0.64 0.48 0 1PUBLICLATRINE 0.25 0.43 0 1PUCCAROOF 0.57 0.50 0 1CEMENTFLOOR 0.57 0.50 0 1GOODFLOOR 0.19 0.39 0 1MULTISTORY 0.21 0.41 0 1GOODVENT 0.43 0.49 0 1BADVENT 0.16 0.36 0 1

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GOODDRAIN 0.42 0.49 0 1BADDRAIN 0.26 0.44 0 1GOODGARBAGE 0.57 0.50 0 1BADGARBAGE 0.26 0.44 0 1GOODCOND 0.50 0.50 0 1BADCOND 0.10 0.30 0 1T5KM 0.33 0.47 0 1T15KM 0.23 0.42 0 1T15PLUSKM 0.07 0.26 0 1FLOOD 0.09 0.29 0 1STREETLIGHT 0.73 0.45 0 1ELECTRICITY 0.96 0.21 0 1MOTORABLE 0.70 0.46 0 1Note: Variable descriptions are available in Table 1.

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Table 3. Descriptive Analysis Across Households’ TenureRENTED OWNED

Household Economics 13,119 618RENT (INR / Month) 1,228COST (INR) 283,088EXP 5,069 6,961

Work CommuteNOTRAVEL 0.37 0.34T15PLUSKM 0.06 0.13MOTORABLEROAD 0.70 0.61STREETLIGHT 0.73 0.62

Dwelling CharacteristicsMULTISTORY 0.38 0.13FLOORAREA 332 705LIVINGROOMS 1.63 3.10GOODVENT 0.42 0.64ATTACHEDBATH 0.48 0.64EXCLUSIVELATRINE 0.52 0.85

Note: variable descriptions are provided in table 1. This table provides average of the variables listed above for specific subsamples. RENT, COST, PRICE and EXP are expressed in currency (Indian Rupees). FLOORAREA is expressed in sq.ft. All other variables are binary (dummy) in nature assuming values of 0 or 1. EXP denotes monthly consumer expenditure of a household excluding housing related costs.

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Table 4. OLS Models of Price (or Rent)Dependent variable: log(PRICE or RENT)

(1) (2)

RENTED -4.718*** -3.493***

(0.03) (0.72)RENTED*DISTRICT Included

PURCHASE 1.027*** 1.091***

(0.10) (0.13)QUARTER -0.289*** -0.296***

(0.02) (0.02)FLOORAREA 0.0005*** 0.001***

(0.00) (0.00)LIVINGROOMS 0.129*** 0.137***

(0.01) (0.01)OTHERROOMS 0.071*** 0.073***

(0.01) (0.01)COVVERAND 0.061*** 0.064***

(0.01) (0.01)UNCOVVERAND -0.011 -0.011

(0.01) (0.01)TAPWATER 0.019 0.017

(0.02) (0.02)ATTBATH 0.162*** 0.163***

(0.02) (0.02)DETBATH 0.148*** 0.145***

(0.02) (0.02)EXCLATRINE 0.286*** 0.310***

(0.03) (0.03)PUBLATRINE -0.017 0.013

(0.03) (0.03)PUCCAROOF 0.122*** 0.115***

(0.01) (0.01)CEMENTFLOOR 0.006 0.007

(0.02) (0.02)GOODFLOOR 0.135*** 0.130***

(0.02) (0.02)MULTISTORY 0.095*** 0.097***

(0.01) (0.01)GOODVENT 0.113*** 0.108***

(0.02) (0.02)BADVENT -0.054*** -0.048***

(0.02) (0.02)GOODDRAIN 0.095*** 0.096***

(0.02) (0.02)BADDRAIN 0.037** 0.027

(0.02) (0.02)GOODGARBAGE 0.068*** 0.078***

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(0.02) (0.02)BADGARBAGE -0.022 0.001

(0.02) (0.02)GOODCOND 0.076*** 0.074***

(0.02) (0.02)BADCOND -0.140*** -0.141***

(0.02) (0.02)T5KM 0.096*** 0.096***

(0.01) (0.01)T15KM 0.142*** 0.145***

(0.02) (0.01)T15PLUSKM 0.156*** 0.158***

(0.02) (0.02)FLOOD 0.019 0.031

(0.02) (0.02)STREETLIGHT 0.027* 0.034**

(0.02) (0.02)ELECTRICITY 0.327*** 0.328***

(0.04) (0.04)MOTORABLE 0.008 0.004

(0.01) (0.01)Constant 9.844*** 8.960***

(0.35) (0.59)457 District controls Included IncludedObservations 13,743 13,743R2 0.828 0.838Adjusted R2 0.821 0.829Residual Std. Error 0.600 (df = 13203) 0.587 (df = 12982)

F Statistic 118.194*** (df = 539; 13203) 88.575*** (df = 760; 12982)

Note: *p<0.1; **p<0.05; ***p<0.01. Quantities in the parentheses denote standard errors.

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Table 5. Probit Models of Tenure Choice (Owning versus Renting)(1) (2) (3) (4) (5) (6)

QCRP -0.044*** -0.014 -0.014 -0.015 -0.014 -0.029***(0.011) (0.011) (0.011) (0.011) (0.011) (0.011)

log(EXP) 0.370*** 0.188***(0.011) (0.015)

HouseholdMALENO -0.113*** -0.112*** -0.111*** -0.109***

(0.012) (0.012) (0.012) (0.012)HHOLDSIZE 0.221*** 0.222*** 0.221*** 0.193***

(0.007) (0.007) (0.007) (0.007)HEADMALE -0.172*** -0.175*** -0.172*** -0.215***

(0.026) (0.026) (0.026) (0.026)Occupation

MANAGER 0.023 -0.037 -0.034 -0.034 -0.035(0.033) (0.034) (0.034) (0.034) (0.034)

TECHNICIAN -0.111*** -0.158*** -0.160*** -0.162*** -0.150***(0.039) (0.040) (0.040) (0.040) (0.040)

CLERK 0.050 -0.007 -0.003 -0.004 0.027(0.041) (0.041) (0.042) (0.042) (0.042)

SHOPWORK -0.320*** -0.363*** -0.352*** -0.354*** -0.273***(0.031) (0.032) (0.032) (0.032) (0.033)

SKLABOR 0.420*** 0.383*** 0.384*** 0.380*** 0.456***(0.057) (0.058) (0.059) (0.059) (0.059)

CRAFTSMAN -0.329*** -0.383*** -0.366*** -0.372*** -0.280***(0.032) (0.033) (0.033) (0.033) (0.034)

OPERATOR -0.381*** -0.462*** -0.451*** -0.455*** -0.369***(0.036) (0.037) (0.037) (0.038) (0.038)

ELEMENT -0.441*** -0.513*** -0.500*** -0.513*** -0.397***(0.033) (0.033) (0.034) (0.034) (0.035)

EmploymentSELFEMP 0.550*** 0.462*** 0.469*** 0.471*** 0.502***

(0.020) (0.020) (0.020) (0.020) (0.020)CASLAB 0.402*** 0.376*** 0.377*** 0.375*** 0.423***

(0.028) (0.028) (0.028) (0.028) (0.029)OTHEREMP 0.136*** 0.284*** 0.284*** 0.282*** 0.362***

(0.034) (0.035) (0.035) (0.035) (0.036)Religion

MUSLIM -0.110*** -0.097*** -0.063**(0.026) (0.026) (0.026)

CHRIST 0.066 0.052 0.044(0.044) (0.045) (0.045)

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SIKH 0.659*** 0.664*** 0.628***(0.073) (0.073) (0.074)

JAIN 0.217* 0.224** 0.160(0.111) (0.111) (0.112)

BUDH -0.047 -0.107 -0.101(0.130) (0.131) (0.132)

CasteSC 0.074*** 0.125***

(0.027) (0.027)ST 0.099** 0.140***

(0.050) (0.050)OBC -0.006 0.034

(0.021) (0.021)Constant -3.02*** 0.234*** -0.279*** -0.32*** -0.33*** -1.95***

(0.132) (0.088) (0.095) (0.095) (0.095) (0.160)Observations 30,717 30,717 30,717 30,717 30,717 30,717Log Likelihood -18,455.9 -18,098.9 -17,118.4 -17,061.4 -17,054.9 -16,975.8Akaike Inf. Crit. 36,981.7 36,287.9 34,332.8 34,228.8 34,221.9 34,065.6

Pseudo R-Squared

McFadden’s 0.08 0.10 0.15 0.15 0.15 0.15Maximum Likelihood 0.10 0.12 0.18 0.18 0.18 0.18Cragg and Uhler’s 0.14 0.17 0.24 0.24 0.24 0.25

Note: *p<0.1; **p<0.05; ***p<0.01. This table shows the results of probit models of housing tenure choice. Household is the unit of analysis. The dependent variables is the binary tenure choice (=1 if owned; =0 if rented)

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Appendix Table 1. OLS Model of Income(1) (2)

MANAGER 0.112*** 0.115***(0.01) (0.01)

TECHNICIAN 0.036*** 0.041***(0.02) (0.01)

CLERK -0.013 -0.003

(0.02) (0.02)SHOPWORK -0.331*** -0.312***

(0.01) (0.01)SKLABOR -0.204*** -0.178***

(0.02) (0.02)CRAFTSMAN -0.389*** -0.352***

(0.01) (0.01)OPERATOR -0.330*** -0.303***

(0.01) (0.01)ELEMENT -0.539*** -0.491***

(0.01) (0.01)

SELFEMP -0.008 -0.008

(0.01) (0.01)CASLAB -0.185*** -0.175***

(0.01) (0.01)OTHEREMP -0.546*** -0.538***

(0.01) (0.01)

SC -0.188***(0.01)

ST -0.151***(0.02)

OBC -0.161***(0.01)

Constant 11.85*** 11.82***(0.14) (0.14)

542 District Controls Included IncludedObservations 44,883 44,883R2 0.281 0.290Adjusted R2 0.272 0.282F Statistic 31.272*** (df = 553; 44329) 32.7 (df = 556; 44326)Note: *p<0.1; **p<0.05; ***p<0.01. Quantities in the parentheses denote standard errors. Dependent variable: Log(INC). The Income estimates are directly proportional to the monthly household expenditure (EXP) reported in the survey. We develop the series of corresponding income (INC) by applying a 30 percent income tax rate and the annual propensity to save15 (APS) estimates based on Burgohain (2009),

15 For the consumption levels included in our study, the total consumption is nearly 65 percent of the total income

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Appendix Table 2. Urban and Rural Homeownership Rates by CasteIndia Rural Urban Urban-Rural Compression

Overall 87% 95% 69% 73%Scheduled Caste 90% 96% 74% 77%Scheduled Tribe 91% 95% 65% 68%Note: Urban-Rural compression is the ratio of a category’s homeownership rate in urban India to that in rural India.

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Acknowledgments: Authors are thankful to the following people for their intellectual contribution or support to this study: Jeremy Isnard, Vinod Singh, Divyanshu Sharma, Minu Agarwal, Vivek Sah, Madalasa Venkatraman, discussants at the American Real Estate Society Annual Meeting- Big Island-Hawai (2012) and CPP Conference-IIM Bangalore (2013), Piyush Tiwari, H K Pradhan, Patrick Smith, and many others.

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