Quantitative and qualitative demand for slum and non-slum housing in Delhi: Empirical evidences from household data

Habitat International 38 (2013) 90e99

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Quantitative and qualitative demand for slum and non-slum housing in Delhi: Empirical evidences from household data Sohail Ahmad a, b, *, Mack Joong Choi c, Jinsoo Ko c a

United Nations University Institute of Advanced Studies (UNU-IAS), 1-1-1 Minato Mirai, Nishi Ku, Yokohama 220-8502, Japan Tokyo Institute of Technology, Meguro, Tokyo, Japan c Graduate School of Environmental Studies, Seoul National University, Republic of Korea b

a b s t r a c t Keywords: Housing demand Price and income elasticities Housings attributes Slum and squatter Delhi

This study estimates quantitative and qualitative demand for housing using household survey data in Delhi. Both housing demand and demand for housing attributes are further stratified by settlement typeeslum and non-slum, and by tenure e owner and renter. The estimation results indicate that housing demand is inelastic with respect to price and income, whereas the magnitude of price elasticity is overall smaller than that of income elasticity in absolute term. In slum households, however, price elasticity is larger than income elasticity. The estimates of housing attributes also show that floor area, availability of a separate kitchen, permanent material of roof, independent latrine, drainage, and flat type of dwelling structure, among others, are important determinants of rent. In slum households, however, only quantity variables matter while quality variables have little effect on rent. This study concludes with policy prescriptions including sufficient urban land supply, which is required to cope with income growth of non-slum households on one hand and to provide low cost dwelling for slum household on the other hand. Ó 2012 Elsevier Ltd. All rights reserved.

Introduction Today, unprecedented in history, we are living in the urban world where urban population surpasses rural. Nevertheless, one-third of the global urban population live in the slum and squatter type of settlements and more than half of them live in Asia (UN-Habitat, 2003). In India, for example, 44% of urban households live in the slum (UN-Habitat, 2008, p. 264). Specially in Delhi, the capital city of India, the urbanization level reached 93% according to 2001 census data whereas housing shortage in urban Delhi was estimated as 1.13 million as on 2007. As a result, about three-fourth of Delhi’s population lives in uncontrolled urban settlements, including slums and squatters (Dupont, Tarlo, & Vidal, 2000; Kumar, 2006, 2008; Sivam, 2003). Urban and housing policies, in an effort to overcome the gap between demand and supply of urban housing, require adequate information about the characteristics of housing demand. In particular, empirical evidences on household preferences at disaggregate level are useful for matching supply with demand effectively. Moreover, estimates for housing demand need to be area-specific because housing market is divided into submarkets by urban area due to location dependency. In this respect, while a number of studies have raised issues of housing supply constraints in Indian * Corresponding author. United Nations University Institute of Advanced Studies (UNU-IAS), 1-1-1 Minato Mirai, Nishi Ku, Yokohama 220-8502, Japan. E-mail address: [email protected] (S. Ahmad). 0197-3975/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.habitatint.2012.02.003

cities (Ahmad & Choi 2009, 2011; Kumar, 2008; Kundu, 2004; Pugh, 1991; Sivam 2003; Srirangam, 2000), empirical literatures on demand side are relatively rare. There are only three studies conducted to estimate housing demand at the national level (Bandyopadhyay, Kuvalekar, Basu, Baid, & Saha, 2008; Dholakia, 1980; Tiwari & Parikh, 1998). And only three studies have appeared at the city level in India, namely, Ahmedabad (Mehta & Mehta,1989), Bangalore (Malpezzi & Tewari, 1991) and Mumbai (Tiwari, Parikh, & Parikh, 1999). Furthermore, little is known on housing market in slum area. Our study attempts to fulfill this gap and helps to understand urban housing market in Delhi. Specifically, this study aims at empirically estimating housing demand, using household survey data in Delhi. Housing demand comprises both quantitative and qualitative levels, where the latter is estimated by demand for housing attributes for the first time for the local housing market in Delhi. In particular, distinguished from previous studies, the estimations are further stratified by slum and non-slum, in order to appropriate policy prescriptions, since there are significant differences in characteristics of household, dwelling and neighborhood between the two types of settlement (for detail see Table 3). This study adopts slum definition by National Sample Survey Organization (NSSO) (2004) which includes JJ clusters1 notified and non-notified 1

JJ stands for the Hindi words jhuggi jhopdi, a colloquial term for the hut of the poor.

S. Ahmad et al. / Habitat International 38 (2013) 90e99

while non-slum includes all other forms of settlementseformal and informal.2 The NSSO (2004: p. 7) defines slum as “a compact area with a collection of poorly built tenements, mostly of temporary nature, crowded together usually with inadequate sanitary and drinking water facilities in unhygienic conditions in that compact area”. Such an area is considered as a slum if at least 20 households live and squatters if less than 20 households live. In addition, all areas notified as slums by the respective municipalities, corporations, local bodies or development authorities are treated as ‘notified slums’. Slum has been considered in urban area only. Separate estimations are also carried out by tenure (owner and renter) in order to compare with the previous studies. This study employs the data extracted from 58th round National Sample Survey (NSS) on ‘housing condition of India’ conducted in 2002 by NSSO, Ministry of Statistics and Programme Implementation. This is the latest available micodata,3 and the 1993 data are also used for an intertemporal comparison purpose. The data was collected through stratified sampling. In 2002, a total of 1781 households were selected randomly from 144 Urban Frame Survey blocks in urban area and 8 villages in rural area for Delhi (NSSO, 2004). Next section describes housing conditions in Delhi, followed by housing demand function and demand for housing attributes are estimated. Finally conclusions section summarizes the findings and discusses policy and planning implications. Housing conditions in Delhi Urbanization in Delhi This section elaborates population growth and spatial expansion to explain urbanization in Delhi between 1951 and 2011. Urban population in Delhi has grown from 1.4 million to 16.3 million in the last six decades (Table 1). During these periods, the average decennial urban growth rate was 49.3%, varied between 21.0 (2001e2011) to 64.2 (1951e1961). The population growth through net in-migration has contributed slightly more than the natural growth in Delhi (Government of NCTD, 2009). Delhi has also experienced urbanization in the form of urban sprawl where its core area has experienced less population growth than its periphery both in 1981e1991 and 1991e2001 (Dupont et al., 2000; Sivaramakrishnan, Kundu, & Singh, 2005). The National Capitals Territory of Delhi’s (NCTD) core area population grew at the rate of 3.6% in 1981e91 while its periphery grew at the rate of 3.8% during the same period. In 1991e2001, the core and periphery area grew at the rate of 3.1 and 4.1%, respectively. The population growth trend is presenting Delhi as a growing city (Kumar, 2006), however, recent census shows remarkably low decennial growth rate (only 21%), even less than half to the last decade, attributed to a combination of declining fertility and mass slum demolitions (TOI, 2011). Therefore, population growth in Delhi should be understood in the context of the growth of Delhi Urban Agglomeration (DUA), for instance, Gurgaon, Faridabad, Ghaziabad and Noida, however, officially these cities are not considered as a part of DUA due to nature of Delhi as a state. These towns can be seen in Fig. 1. In term of physical expansion, Delhi has expanded from 201 km2 to 792 km2 between 1951 and 2009. Historically, it expanded through numerous events but major expansions took place during the re-establishment of Delhi as the capital of British India and consequences of partition. After enactment of the Delhi

2 Non-slum also includes informal settlements, for instance, rural and urban villages, unauthorized colonies and JJ resettlements colonies (NSSO, 2004). 3 The next round survey was conducted during 2008e2009, however, row data was not available at the time of this study.

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Table 1 Population growth of Delhi, 1951e2011. Years

Total population

Total urban population

Urban population %

Decennial urban growth %

1951 1961 1971 1981 1991 2001 2011*

1,744,072 2,658,612 4,065,698 6,220,406 9,420,644 13,782,976 16,752,235

1,437,134 2,359,408 3,647,023 5,768,200 8,471,625 12,819,761 16,333,915

82.4 88.8 89.7 92.7 89.9 93.0 97.5

e 64.2 54.6 58.2 46.9 51.3 21.0

Note: *represents provisional data. Source: Data from various censuses of India.

Development Authority (DDA) Act 1957 and consequently, planning intervention led to expansion up to 326.55 km2 in 1961, about 62.1% decennial growth. In 1990s, it was expanded to 624.28 km2 and today (2009) it is about 792 km2 out of total 1483 km2 area of NCTD, which approximately 53.4% area is urbanized. It is estimated that by 2021, all the area under NCTD will be urbanized (Government of India, 2007b). Fig. 2 shows urban and rural areas in NCTD, including various administrative controls over areas. The spatial spread of administrative control provides, somehow, difficulties in city governance, including provision of physical infrastructures. So far, we have discussed urbanization in Delhi both in terms of urban population growth and physical expansion; however our study demands to present disaggregated population by settlement types. In this reference, Delhi water Supply and Sewerage Project Preparation Study Report (as cited in Government of NCTD 2006: p. 364) estimated that slum (JJ clusters and slum designated areas) population was 5.26 million which is expected to increase by 6.45 million in 2011 and 7.8 million in 2021 (Table 2). Although, the population projection for 2011 is over projected, nevertheless, it emphasizes severe problem of slum growth in Delhi. Table 2 also presents perceived level of tenure security in various types of settlements in Delhi. It showseJJ clusters and unauthorized colonies: low; slum designated areas, regularized unauthorized colonies, urban villages and rural villages: medium; and resettlement colonies and planned colonies: highedegree of tenure security. Fig. 3 shows spatial spread of slum populationehighly concentrated in central part of Delhi. Intertemporal changes The NSS data provide various information regarding both dwelling and household characteristics. Table 3 summarizes these housing and household characteristics in Delhi by settlement type and tenure, as measured by means and frequency distributions from 1993 and 2002 data. The 1993 and 2002 NSS data may not be directly comparable since they are not panel data derived from repeated sample. Nevertheless intertemporal changes indicate that housing conditions in Delhi have not been improved, but rather tend to have worsened over time. As the most representative index, mean of total floor area each household occupies decreased from 1993 to 2002, even though average monthly consumption expenditure of households, which may be used as an income proxy, increased over the same period. The NSS data also contain price information: rent for the renter housing and imputed rent for the owner-occupied housing. It is noticeable that rent per unit area (square meters) increased significantly between 1993 and 2002 in line with the decrease in floor area. At the same time, housing quality, as represented by availability of separate kitchen, drinking water within premises and motorable access road for example, tend to have decreased over time. Therefore, housing market in Delhi is characterized over time by increase in housing price and decrease in housing consumption in both quantitative and qualitative terms.

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S. Ahmad et al. / Habitat International 38 (2013) 90e99

Fig. 1. Delhi metropolitan area in national capital region. Source: Government of India, 2007a.

Characteristics by settlement type By settlement type, it is clear that housing condition of slum dwellings is far inferior to that of non-slum dwelling. Moreover, slum area has overall experienced more severe deterioration of housing condition over time than non-slum area. As the result, slum dwellings are smaller in size than non-slum dwellings, as measured by total floor area. The number of living room is also fewer in slum than non-slum housing. Therefore, as there is not much difference in household size between slum and non-slum households, members per living room is higher in slum than non-slum dwellings, indicating overcrowded housing condition in slum settlements. Besides these quantitative measures of housing consumption, slum dwellings have much lower qualities than non-slum dwellings, particularly in terms of roof material and availability of a separate kitchen, drinking water within premise, drainage, independent latrine, and motorable access road. Among these housing attributes, shelters in slum severely lack permanent roof, separate kitchen, drinking water within premise, drainage, and independent latrine. Only permanent materials of floor and wall as well as tap water are relatively widely observed in slum shelters. Related to these housing qualities, it is characteristic that a great portion of slum dwellings fall neither independent nor flat type while independent type decreased over time. In comparison, flat type of dwelling unit increased over time and composes relatively large portion for non-slum housing. Meanwhile, the proportion of

housing used exclusively for residence use is rather higher in slum than non-slum dwellings, indicating relatively little mixed use. Despite the quantitative and qualitative differences in housing consumption, however, there is not much difference in rent per square meters between slum and non-slum dwellings. In comparison, households of slum area have much lower average monthly consumption expenditure than those of non-slum area, reflecting their lower income level. Meanwhile, male headed households prevail, though the proportion of female-headed household is slightly lower in slum than non-slum households. On the contrary, social group shows a distinctive distribution pattern between slum and nonslum households: the majority of slum households are composed of lower social group while upper social group constitutes the majority of non-slum households. In this study, Scheduled Caste (SC) and Scheduled Tribe (ST) households are named as the lower social group, Other Backward Classes (OBCs) as the middle social group and the rest as the upper social group, as Indian literatures use these abbreviations comfortably in the academic discourses (Baud, Pfeffer, Sridharan, & Nainan, 2009; Baud, Sridharan, & Pfeffer, 2008). However, interpretations should be done with due consideration of facts and figures as well as long standing affirmative discrimination4 to the SC and ST, and the OBCs households. Nevertheless, various studies have categorically pointed out that the SC and ST households are in lower ladder, the

4 For instance, reservation for education and jobs, and other venue like, in electoral democracy.

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Fig. 2. Towns of Delhi, 2001. Source: Authors’ digitization.

OBCs are in middle and the others (except mentioned one) are in upper ladder in consumption expenditure or wealth (for detail discussion see Zacharias & Vakulabharanam, 2011). Characteristics by tenure Meanwhile, by tenure, renter housing is smaller than owneroccupied housing in terms of both total floor area and number of living room. Since renters also have smaller member of a household than owners, however, there is little indication of overcrowding in

Table 2 Settlement-wise projected population in Delhi 2004e2021 (in millions). 2004

2005

2006

2011

2021

(S), # (S), ## (NS), ### (NS), # (NS), ##

2.30 2.96 1.97 0.82 1.97

2.37 3.05 2.04 0.85 2.04

2.45 3.15 2.10 0.87 2.10

2.82 3.63 2.42 1.01 2.42

3.41 4.39 2.93 1.22 2.93

(NS), ## (NS), ### (NC), ##

0.99 3.67 0.82 15.50

1.11 3.79 0.85 16.09

1.05 3.91 0.87 16.50

1.21 4.50 1.01 19.00

1.46 5.44 1.22 23.00

Type of settlements

Nature and tenure security*

JJ Clusters Slum Designated Areas Resettlement Colonies Unauthorized Colonies Regularized Unauthorized Colonies Urban Villages Planned colonies Rural Villages Total Population

renter housing in comparison to owner-occupied housing, as measured by household members per living room. With regard to housing qualities, it is observed that rental housing is poorer than owner-occupied housing in terms of separate kitchen and latrine. Except those two attributes, however, renter housing is pretty much comparable to owner-occupied housing in qualitative attributes. For dwelling unit type, it is characteristic that flat and other type account for relatively large portion in rental housing whereas owner-occupied housing is composed largely of independent type. Rent per square meters is lower in rental housing than owneroccupied housing. In parallel, consumption expenditure is also lower in renter than owner households, indicating that renters belong to lower income bracket. On the contrary, there is no distinctive difference in composition of social group between renters and owners. Estimation of housing demand

Notes: *: Authors’ input; S: slum, NS: non-slum, and NC: not considered for this study; tenure security e high: ###, medium: ##, and low: #. Source: Delhi Water Supply and Sewerage Project Preparation Study Report (as cited in Government of NCTD, 2006).

Housing demand function Housing demand function can be written as Eq. (1) where Q is the quantity of the housing demanded, P is the unit price of housing, Y is the income of the household, and Z is the vector of household’s demographic and socioeconomic characteristics. This function is widely used in the housing economics literature and some of the pioneer works, for instance, Malpezzi (1999, 2001) can be referred.

Q ¼ Q ðP; Y : ZÞ

(1)

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Fig. 3. Slum population of Delhi municipal population and NDMC areas (total slum population: 1,891,673), 2001. Source: Government of India, 2007c, p. 7.

Table 3 Housing and household characteristics in Delhi by settlement type and tenure, 1993e2002. Non-slum 1993 N Household size Female-headed (%) Social group

Lower (%) Middle (%) Upper (%) Consumption expenditure (Rs/month) Total floor area (m2) Number of living room Member per room Rent (Rs/m2-month) Dwelling Independent (%) Unit Flat (%) Type Others (%) Exclusive residence (%) Permanent material Floor (%) Wall (%) Roof (%) Separate kitchen (%) Drinking water Tap (%) Within premise (%) Permanent drainage (%) Independent latrine (%) Motorable access road (%)

664 4.7 7.2 13.1 86.9 3063 45.4 2.2 2.3 28.0 48.8 26.8 24.4 87.6 96.2 99.1 72.1 66.6 90.7 94.7 61.6 59.7 92.3

Slum 2002 1,468 4.2 8.0 35.3 16.9 64.7 5290 31.0 1.8 2.2 75.3 40.4 38.6 21.0 88.8 98.8 99.0 80.4 59.3 90.4 92.6 68.1 64.7 62.5

1993 292 4.3 4.8 41.4 58.6 1656 21.4 1.2 3.2 27.8 64.4 2.4 33.2 85.6 82.1 92.4 35.1 14.4 83.9 65.1 36.0 13.4 60.3

Owner 2002 217 4.0 4.7 75.1 20.8 24.9 2499 8.6 1.1 3.7 65.5 44.7 1.8 53.5 97.7 87.1 85.7 5.1 4.2 84.1 11.5 6.0 2.3 24.9

1993 569 5.3 6.3 26.9 73.1 2860 45.4 2.2 2.7 30.8 67.5 10.5 22.0 89.8 92.1 94.7 57.1 52.7 92.1 84.0 45.5 50.1 83.8

Renter 2002 966 5.0 7.6 41.0 13.5 59.0 5815 34.7 2.0 2.5 85.4 58.3 26.0 16.2 80.1 96.5 95.9 66.8 62.5 89.5 78.6 57.0 67.6 54.7

1993 389 3.5 6.7 41.1 85.9 2308 27.2 1.5 2.4 23.5 33.2 32.4 34.4 82.8 91.7 99.5 66.0 47.3 83.4 87.9 65.6 39.1 80.6

Pooled 2002

2002

720 3.1 7.6 39.7 22.2 60.3 3741 19.4 1.4 2.2 58.9 17.7 45.1 37.3 85.1 98.3 99.0 76.0 38.4 88.6 86.8 64.3 41.9 61.7

1685 4.1 7.6 40.4 17.2 59.6 4929 28.6 1.7 2.4 74.0 41.0 33.8 25.2 89.9 97.3 97.2 70.7 52.2 52.2 82.1 60.1 56.6 57.7

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Table 4 Estimation results of housing demand in Delhi, 2002. Pooled

Non-slum

Coef. ln_rent (Rs/m2-month) ln_consumption expenditure (Rs/month) ln_household size Household head (female ¼ 1) Middle group (lower ¼ 0) Upper group (lower ¼ 0) Settlement type (non-slum ¼ 1) Intercept N Adjusted R2 Mean VIF

0.310 0.881 0.017 0.083 0.043 0.126 0.519 4.273 1646 0.64 1.62

Slum

Beta

P>t

Coef.

Beta.

P>t

0.238 0.755 0.013 0.025 0.018 0.070 0.198 e

0.001*** 0.001*** 0.541 0.096* 0.319 0.001*** 0.001*** 0.001***

0.300 0.895 0.019 0.081 0.054 0.119 e 3.928 1430 0.59 1.69

0.249 0.805 0.015 0.026 0.024 0.067

0.001*** 0.001*** 0.523 0.127 0.273 0.003***

e

0.001***

Owner Coef. ln_rent (Rs/m2-month) ln_consumption expenditure (Rs/month) ln_household size Household head (female ¼ 1) Middle group (lower ¼ 0) Upper group (lower ¼ 0) Settlement type (non-slum ¼ 1) Intercept N Adjusted R2 Mean VIF

0.522 0.817 0.309 0.081 0.127 0.206 0.873 3.091 940 0.73 1.49

Coef. 0.774 0.091 0.099 0.096 0.104 0.052 e 2.504 192 0.60 1.96

Beta

P>t

0.709 0.090 0.130 0.044 0.087 0.044

0.001*** 0.312 0.145 0.348 0.070* 0.363

e

0.001***

Renter Beta.

P>t

0.335 0.623 0.174 0.024 0.049 0.114 0.397 e

0.001*** 0.001*** 0.001*** 0.157 0.012** 0.001*** 0.001*** 0.001***

Coef. 0.305 0.843 0.064 0.094 0.093 0.143 0.271 3.911 706 0.59 1.69

Beta.

P>t

0.291 0.838 0.062 0.032 0.050 0.090 0.064 e

0.001*** 0.001*** 0.088* 0.189 0.123 0.006*** 0.010*** 0.001***

Notes: Dependent variable is log (total floor area) in square meters. ***p-value < 0.01, **: p-value < 0.05, *: p-value < 0.1.

The quantity of the housing demanded (Q) is measured by total floor area. The unit price of the housing (P) is computed using rent for the renter housing and imputed rent for the owner-occupied housing, since the NSS data provide such detailed information. Both rent and imputed rent are measured in terms of monthly rent per unit area (square meters). The NSS data, however, do not contain any information about income of the household (Y). Therefore, we take average monthly consumption expenditure as a proxy variable for permanent income. Since housing is a durable good, permanent income, which can be measured by consumption expenditure as suggested by Friedman, can provide a better measure in explaining housing demand than current income. Meanwhile, household characteristics (Z) include household size (total member of a household), gender of household head, and social group. The slum and non-slum settlement type, one of our main concerns, is also included as an independent variable, as necessary. Housing demand function is estimated using the 2002 NSS data. We employ the log-linear functional form where both dependent variable Q and independent variables P and Y take in logarithmic form so that the estimated coefficients can be directly interpreted as the price and income elasticity of housing demand. Logarithmic form is also taken for household size in order to adjust skewed distribution with a long tail on the right hand side and thereby enhance normality of the distribution.5 Other independent variables are treated as dummy variables. Similar form and proxy variables are often selected, particularly studies originated from developing countries, where data are scarce (Arimah, 1992; Mehta, 1989; Tiwari, 1999). In total, five regression models are used for housing demand estimations e pooled, stratified by settlement type (non-slum and slum) and stratified by tenure (owner and renter). The sample size for pooled, non-slum, slum, owner and renter models are 1,646,

5 Single member of a household accounts for 18.6%, two members 7.8%, three 10. 6%, four 20.1%, and five and more 42.8%.

1,430, 192, 940 and 706 respectively. Regressions are all based upon Ordinary Least Squares (OLS) method. Estimation results Estimations results are presented in Table 4.6 All models show a modestly high level of goodness to fit with adjusted R2 ranged from 0.59 to 0.73. There is no serious multicollinearity problem, as indicated by the mean Variance Inflation Factor (VIF). Price and income elasticities The estimates of price elasticity of housing demand are statistically significant in all of five models. The price elasticity is estimated to be 0.31 in pooled data, indicating that housing consumption in Delhi is overall very inelastic to price. There are significant variations, however, depending upon subgroups. The price elasticity of slum households (0.77) is much higher than non-slum (0.31) and that of renter households (0.52) is greater than owner households (0.30). These results imply that slum and renter households are yet more sensitive to price changes in adjusting their housing consumption level. Meanwhile, the estimate of income elasticity of demand is 0.88 in pooled data, which is higher than price elasticity in absolute term but still a little bit inelastic. Therefore, we can understand that households in Delhi adjust their housing consumption more sensitively to income changes than price changes, though both adjustments are not elastic. However, it is striking that the income elasticity of slum households is too low to be statistically significant. Taking into consideration that slum households have on the average only about a half of income but need to support similar number of a household in comparison to non-slum households, this result may imply that slum households have little room to spend additional income on housing consumption. In comparison, there is

6

In Tables 2 and 4, beta means standardized coefficient.

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Table 5 Previous estimates of income and price elasticities of housing demand in India. Author

Dholakia (1980) Tiwari and Parikh (1998) Bandyopadhyay et al. (2008) Malpezzi and Tewari (1991), p. 160 Mehta and Mehta (1989) Tiwari and Parikh (1997) Tiwari et al. (1999)

Study area (survey period)

Urban India (1988) Urban India (1987/88) India (not known) Bangalore (1975) Ahmedabad (1988) Mumbai (1987/88) Mumbaia (1987/88)

Price elasticity

Income elasticity

Pooled

Owner

Renter

Pooled

Owner

Renter

0.33 1 0.46 e e 0.47 1.2

e 1 e e 0.4 0.21 0.85

e 1 e e 0.8 0.75 1.02

0.58 0.75 0.6 e e 0.34 1.12

e 0.9 e 0.43 0.2 0.33 1.18

e 0.9 e 0.58 0.17e0.43 0.38 1.07

Notes: All values are significant up to 90 percent confidence level. a The data set for this study is similar to (Tiwari & Parikh, 1997), however, this study used permanent income for the estimation of housing demand rather than current income.

not much difference in the income elasticity between owner (0.82) and renter households (0.84). These estimates of price and income elasticities in Delhi are somewhat consistent with previous results found in global and Indian context, although previous estimates did not pay an attention to the differences between slum and non-slum. For developing countries, both price and income elasticities have generally been estimated to be inelastic as our estimates are, even though estimates of price elasticities are relatively rare due to difficulty in measuring prices (Malpezzi, 1991; Malpezzi & Mayo, 1987). Furthermore, our estimates match with the findings that owner and renter income elasticities are similar (Malpezzi & Mayo, 1987). More specifically, Table 5 summarizes previous estimates of price and income elasticities in India as a whole as well as Bangalore, Ahmedabad, and Mumbai, all of which are based upon the data dating back up to the late 1980s. Compared to the previous estimates of price elasticities ranged from 0.33 to 1.2, our estimate for Delhi (0.31) is on the lowest end. This result might reflect to some degree the geographical or/and intertemporal differences as our estimate is derived from Delhi in 2002. Nevertheless our estimate is consistent with previous results in that price elasticities of renter households are in general greater than those of owner households. In previous studies, Income elasticities range from 0.34 to 1.12, similar with price elasticities in absolute term. Compared to these, our estimate (0.88) is relatively high, though it is still within the range. This result might also be attributed to the geographical or/ and temporal characteristics hinged on Delhi and the early 2000s. But another explanation would be permanent income on which our estimate is based, because the estimates based on current income tend to be downward biased (Mayo, 1981).7 Nevertheless, both our and previous estimates show that there is little difference in income elasticities between owner and renter households. Household and settlement characteristics The standardized coefficients (beta) show that income is generally the most important variable and price is the second most important variable in terms of explanatory power of housing demand. However, household characteristics such as household size, gender of household head, and social strata also affect more or less housing demand across the models. The effect of household size on housing consumption is statistically insignificant and even negative in owner and renter households.8 Although this finding is contrary to our expectation that the

7 As the representative case, Tiwari and Parikh (1997) clearly employ the current income and obtain the lowest income elasticity, 0.34, in Table 5. 8 In owner and renter households, housing consumption associated with change in household size are still very much inelastic, as the coefficients range from 0.06 to 0.31.

dwelling size increases with an increase of household size, similar findings are also reported in other studies from India as well as other developing countries. The plausible reasoning for this output is that households may change their consumption basket rather than adjusting dwelling size in response to the number of household (Malpezzi & Mayo, 1987; Mehta & Mehta, 1989). Meanwhile, female-headed households tend to occupy bigger housing than male headed households, but this is only marginally significant in pooled data with the difference between female and male headed households accounted for only 1.09(¼exp(0.083)) times. Estimation results also show that households of upper social group consume more floor area than lower social group. In pooled data, for example, upper group consumes 1.13(¼exp(0.126)) times larger floor area than lower group. Perhaps this is why presence of lower group households in an Indian city is taken as an indicator for deprivation in social capital (Baud et al., 2008, 2009) or neighborhoods quality (Mehta & Mehta, 1989). The very similar group-based differences are also noted in other study of Delhi (Ahmad & Choi, 2009; Dupont, 2004). In comparison, the differences between middle and lower social groups are not much significant. Moreover, in slum, the differences by social groups are relatively marginal. It is rather noteworthy that the bigger differences in housing consumption appear by settlement type than household types: slum households consume less floor space than non-slum households, ceteris paribus i.e. provided other independent variables of price, income, and household characteristics constant. In pooled data, non-slum households consume about 1.68(¼exp(0.519)) times larger floor area than slum households. The difference is even larger among owner households with the ratio of 2.39(¼exp(0.873)), while the ratio decreases to 1.31(¼exp(0.271)) among renter households. Estimation of demand for housing attributes Demand function for housing attributes The theoretical underpinning of estimation of demand for housing attributes has been highlighted by many housing analysts, hence we restrict to fundamental form of the model (for detail see Malpezzi, 1999, 2001). Households are assumed to consume a bundle of housing attributes H(h1, h2, . hn) as well as other composite commodities X whose price is unity. Accordingly, they maximize the utility U(h1, h2, . hn:X) subject to a budget constraint Y ¼ X þ P(H) where Y is the household income and P(H) is nonlinear housing expenditure function. Therefore, demand for housing attributes can be estimated in the form of hedonic price function such as Eq. (2), through which the marginal valuation of each housing attribute is estimated by regressing housing expenditure or price P on housing attributes h1, h2, . hn (Malpezzi, 2001)

P ¼ Pðh1 ; h2 ; .; hn Þ

(2)

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Table 6 Estimation results of demand for housing attributes in Delhi, 2002. Pooled

Non-slum

Coef. Total floor area (m2) Number of living room Dwelling typeeindependent (others ¼ 0) Dwelling typeeflat (others ¼ 0) Use of house (residence only ¼ 1) Floor material (permanent ¼ 1) Wall material (permanent ¼ 1) Roof material (permanent ¼ 1) Kitchen (separate ¼ 1) Drinking water (tap ¼ 1) Drinking water location (within premise ¼ 1) Drainage (permanent ¼ 1) Latrine (independent ¼ 1) Access road (motorable ¼ 1) Settlement type(non-slum ¼ 1) Intercept N Adjusted R2 Mean VIF

0.006 0.025 0.067 0.148 0.148 0.189 0.135 0.264 0.248 0.148 0.116 0.154 0.193 0.052 0.483 1.233 1544 0.19 1.90

Beta

P>t

0.252 0.038 0.049 0.105 0.063 0.045 0.029 0.172 0.182 0.067 0.062 0.111 0.140 0.038 0.218 e

0.001*** 0.302 0.141 0.003*** 0.008*** 0.138 0.339 0.001*** 0.001*** 0.006*** 0.078* 0.001*** 0.001*** 0.127 0.001*** 0.001***

Coef. 0.006 0.023 0.088 0.181 0.137 0.223 0.353 0.280 0.249 0.142 0.163 0.139 0.192 0.044 e 0.885 1383 0.20 1.66

Slum Beta.

P>t

0.242 0.033 0.062 0.127 0.059 0.033 0.041 0.157 0.173 0.059 0.059 0.092 0.129 0.031

0.001*** 0.372 0.096* 0.001*** 0.017** 0.253 0.151 0.001*** 0.001*** 0.019** 0.028** 0.001*** 0.001*** 0.231

e

0.001***

owner Coef. Total floor area (m2) Number of living room Dwelling typeeindependent (others ¼ 0) Dwelling typeeflat (others ¼ 0) Use of house (residence only ¼ 1) Floor material (permanent ¼ 1) Wall material (permanent ¼ 1) Roof material (permanent ¼ 1) Kitchen (separate ¼ 1) Drinking water (tap ¼ 1) Drinking water location (within premise ¼ 1) Drainage (permanent ¼ 1) Latrine (independent ¼ 1) Access road (motorable ¼ 1) Settlement type (non-slum ¼ 1) Intercept N Adjusted R2 Mean VIF

0.006 0.034 0.058 0.176 0.082 0.263 0.091 0.283 0.155 0.128 0.260 0.329 0.171 0.128 0.515 1.343 899 0.37 2.26

Coef. 0.056 0.353 0.015 e 0.133 0.123 0.049 0.055 0.201 0.108 0.051 0.047 0.028 0.085 e 1.353 161 0.37 1.81

Beta

P>t

0.781 0.314 0.018

0.001*** 0.004*** 0.806

0.025 0.110 0.042 0.032 0.104 0.102 0.035 0.009 0.010 0.091 e

0.774 0.229 0.619 0.662 0.179 0.210 0.629 0.888 0.910 0.203 0.008***

renter Beta.

P>t

0.326 0.065 0.050 0.136 0.035 0.082 0.027 0.226 0.128 0.067 0.177 0.282 0.135 0.110 0.333 e

0.001*** 0.100 0.272 0.004*** 0.192 0.019** 0.426 0.001*** 0.001*** 0.020** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001***

Coef. 0.014 0.147 0.111 0.253 0.151 0.011 0.269 0.341 0.159 0.148 0.049 0.053 0.048 0.059 0.165 1.247 645 0.15 1.70

Beta.

P>t

0.368 0.151 0.056 0.171 0.068 0.002 0.035 0.191 0.106 0.063 0.021 0.034 0.032 0.039 0.031 e

0.001*** 0.012** 0.191 0.001*** 0.078* 0.970 0.502 0.001*** 0.045** 0.108 0.624 0.451 0.496 0.319 0.425 0.001***

Notes: Dependent variable is log (rent per square meter per month) in rupees. ***p-value < .01, **: p-value < .05, *: p-value < .1.

For dependent variable, the housing price (P) is defined in the same way as the case of housing demand estimation: this study uses rent for the renter housing and imputed rent for the owner housing, both measured by monthly rent per square meters. As independent variables, a variety of housing attributes are collected from the NSS data as shown in Table 3. Total floor area, employed for housing demand function, as well as number of living room are taken to capture the quantitative characteristics of dwelling. Moreover, various quality measures of structure and facilities are taken, including material of floor, wall, and roof as well as availability of a separate kitchen, tap water, drinking water within premise, drainage, independent latrine, and motorable access road. Shelter typology is distinguished by structural type of dwelling unit (independent, flat, or others) and use of house (exclusive residence or mixed). Finally, settlement type (slum and non-slum) is also taken into consideration in order to reflect collectively neighborhood quality. We specify Eq. (2) into semi-log functional form by taking logarithmic form for dependent variable P in order to enhance normality of the distribution. Independent variables related to housing qualities, except those associated with quantitative characteristics, are treated as dummy variables. Similar to the study of housing demand function, we employ five regression models

i.e. pooled, stratified by settlement type (non-slum and slum) and by tenure (owner and renter), all based upon OLS method. In comparison to housing demand estimation, sample size is slightly reduced due to the use of large number of independent variables which have more missing values.9 Estimation results Estimations results are summarized in Table 6. Goodness to fit ranges from 0.15 to 0.37 in terms of adjusted R2 across the models. No serious multicollinearity problem is found. Effect of each housing attribute on rent Since there are many independent variables, primary attention is given to those statistically significant up to 90 percent level. Regarding the effect of space related quantitative variables, it is noteworthy that rent per square meters decreases as floor area increases in all five models. This result implies that relatively smallsized dwellings are more valuable than large-sized dwellings in

9 Flat type of dwelling unit is also removed from slum model because there are only 4 observations.

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Delhi housing market. Inversely, it is also found that, with an increase in the number of living room, rent per square meters (hereafter ‘rent’) increases in slum and renter housing. These two results together may imply that, in slum and renter housing, number of living room is more valuable that total floor area, even though each living room has relatively small size. This would be particularly so, taking account that about four family members reside in only one living room on the average in slum households. Among the housing quality related variables, floor and wall materials are not statistically significant, reflecting that the majority of dwellings are equipped with permanent material regardless of settlement and tenure types, as shown in the descriptive statistics of Table 3. In comparison, whether to use permanent material for roof is a significant determinant of rent, except in slum settlement. Likewise, availability of a separate kitchen significantly affects rent, except in slum settlement. Furthermore, availability of tap as a source of drinking water, location of drinking water within premises, permanent drainage, and an independent latrine also type fetches higher rent, except in slum and renter housing. On the contrary, motorable access road is significant only in owner-occupied housing. For the structural type of dwelling unit, it is generally found that flat type fetches higher rent than other types. In comparison, independent type is largely indifferent from other types in determining rent. These results together illustrate that flat type of dwelling unit is preferred to independent type in Delhi housing market. Regarding the use of house, those dwellings used for residence only are generally more valuable that those dwellings used for mixed purposes. In sum, based on the pooled model, the standardized coefficients (beta) illustrate that the importance of each independent variable in explaining rent variation is in the order of floor area, separate kitchen, roof material, independent latrine, drainage, flat type of dwelling structure, and etc. Meanwhile, dwellings in slum settlement fetch 48 less value than those in non-slum, reflecting lower neighborhood quality in collective terms. Differences among pooled, slum/non-slum, owner/renter housing Comparing five models, it is notable that, in slum settlement, only the two dwelling space related variables are significant in determining rent, while other quality related variables have little effect on rent. This result probably indicates that housing quality is generally so poor in slum settlements that quality differentials among slum houses may have merely marginal effect on rent. Accordingly, housing quantity is still far more important than quality in slum housing’s valuation, particularly taking into consideration that, with only the two quantitative variables significant, the slum model posses the highest explanatory power of about 37% among five models. In this regard, it is noteworthy that addition of a living room fetches 35% more dwelling value for slum housing. In the similar context, it is also notable that determinants of renter housing are relatively few: in addition to the two quantitative variables, only roof material, separate kitchen, flat type structure, and exclusive use of residence have significant effects on rent. This result might also reflect that quality of renter housing is relatively poor and therefore there is not much quality differentials among renter housing enough to affect rent. Conclusions This study empirically estimated both housing demand and demand for housing attributes, employing household survey data in Delhi. Compared to previous studies, it particularly paid an attention to the differences between slum and non-slum settlements, based upon stratified estimations.

Over time, households have experienced reduced consumption of mean floor area although income has improved significantly. In term of housing quality, availability of a separate kitchen, drinking water within premises and motorable access road has been deteriorated as well. In particular, these qualities have remarkably degraded in slum households. Slum households have also experienced severe overcrowding. The estimation results of housing demand show that housing demand is inelastic with price and income. Overall, the magnitude of price elasticity is smaller than that of income in absolute term, which indicates that housing demand is less responsive to change in price of dwelling than household income, except in slum. Taking into consideration that the current housing consumption in Delhi is close to the minimum level of basic needs, this implies that there is little room to reduce housing consumption further despite a rise of rent on one hand. On the other hand, this also implies that the increase in household income is relatively effective in improving housing consumption. Therefore, economic growth of India and particularly Delhi in general, or any specific income improvement program, is expected to boost housing consumption. However, in the case of slum settlements, a story may be totally different. Income elasticity of slum households is too inelastic to have a significant effect on housing consumption, while price elasticity of slum households is greater than that of non-slum households. Therefore, general economic policies to boost income growth will not instantly help slum households improve their housing consumption, since their current priorities of spending might not be on housing with increased income. Rather specific measures to provide low cost housing are required to improve housing consumption. In this regard, the government needs to focus primarily on provision of low cost serviceable land while increasing urban land supply in Delhi. In addition, by tenure, the renter households have high income elasticity as well as low price elasticity in comparison to owner households, with income elasticity is larger than price elasticity in absolute term. Therefore, if the government has the capacity to provide the subsidy for renter households who consume only about a half of floor area in comparison to owner households, income subsidy would be more effective that rent subsidy in enhancing housing consumption. Meanwhile, the estimates of housing attributes suggest that there still be a need to supply more of relatively small-sized dwellings with flat type of structure in Delhi housing market, while an attention also needs to be given to securing more of living rooms for slum and renter housing. In terms of housing quality, separate kitchen, permanent materials of roof, independent latrine, and drainage, among others, are generally most important attributes to be provided. Nevertheless it is noticeable that housing qualities do not matter for slum households. They have willingness to pay only for housing quantity, reflecting the absolute shortage of floor area and number of living room and the resulting overcrowding. In this regard, focusing on the fact that an addition of a room in the dwelling of slum households improve significant dwelling value, an incremental housing development is recommended in order to allow an add-up of living rooms incrementally for structurally feasible and available un-built floor space in dwelling of slum. Therefore, in sum, a policy combination is desirable for slum dwellings: provision of low cost dwellings, primarily attributed to low cost land, as well as promotion of incremental dwelling. These two may also be combined in the context of traditional site and service and self-help housing approach. In comparison, housing qualities are important determinants of dwelling value for non-slum households. Therefore, in sum,

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provision of adequate infrastructures is also necessary in addition to an increase in income in order to enhance housing consumption for non-slum households. In this context, since it is generally expected that, by observing present economic scenario of India and particularly Delhi, income is bound to increase, there should be sufficient housing supply in Delhi in order to cope with an increase in housing demand coupled with income growth in nonslum households. This in turn requires sufficient urban land supply. Therefore, government policies need to be focused commonly and ultimately on enabling and encouraging strategies to increase urban land supply for both non-slum and slum households: It is necessary to increase dwelling stocks equipped with adequate infrastructures to cope with increasing housing demand derived from income growth for non-slum households on one hand. It is also necessary to provide low cost serviceable land, coupled with incremental dwelling strategy, for slum households on the other hand. Acknowledgements The authors are thankful to the anonymous reviewers for their useful feedback and also thankful to the National Sample Survey Organization, India, for providing data sets used in this study. The article is the sole responsibility of the authors and does not express the views of the institutions authors are affiliated. References Ahmad, S., & Choi, M. J. (2009). Problems and prospects of uncontrolled urban settlements in developing countries: a case study of Delhi. In Proceedings of Asian Planning School Association (APSA) Congress 2009, Ahmedabad, India. Ahmad, S., & Choi, M. J. (2011). The context of uncontrolled urban settlements in Delhi. ASIEN, 118, 75e90. Arimah, B. C. (1992). Hedonic prices and the demand for housing attributes in a third world city: the case of Ibadan, Nigeria. Urban studies, 29(5), 639e651. Bandyopadhyay, A., Kuvalekar, S. V., Basu, S., Baid, S., & Saha, A. (2008). A study of residential housing demand in India. NHBeNIBM. Monograph. Baud, I., Pfeffer, K., Sridharan, N., & Nainan, N. (2009). Matching deprivation mapping to urban governance in three Indian mega-cities. Habitat International, 33(4), 365e377. Baud, I., Sridharan, N., & Pfeffer, K. (2008). Mapping urban poverty for local governance in an Indian mega-city: the case of Delhi. Urban Studies, 45(7), 1385e1412. Dholakia, B. (1980). The economics of housing in India. New Delhi: NBO. Dupont, V. (2004). Socio-spatial differentiation and residential segregation in Delhi: a question of scale. Geoforum, 35(2), 157e175. Dupont, V., Tarlo, E., & Vidal, D. (2000). Delhi: Urban space and human destinies. New Delhi: Manohar Pubns.

99

Government of India. (2007a). Evaluation study of DMA towns in national capital region (ncr) town and country planning organization. Ministry of Urban Development. Available from http://urbanindia.nic.in/theministry/subordinateoff/ tcpo/DMA_Report/COVER_PAGE.pdf Accessed 05.01.11. Government of India. (2007b). Master plan for Delhi, 2021. New Delhi: Delhi Development Authority, Ministry of Urban Development. Government of India. (2007c). DelhiePresentation of thematic maps on 2001 census data at ward level, map division. India: Office of the Registrar General. Government of the National Capital Territory of Delhi. (2006). Economic survey of Delhi, 2005e06. New Delhi: Planning Department. Government of the National Capital Territory of Delhi. (2009). Economic survey of Delhi, 2008e09. New Delhi: Planning Department, Development, GOI. 172. Kumar, A. (2006). Violating, shortchanging and confusing: implications of planning a growing city on the poor. ITPI Journal, 3(1), 14e30. Kumar, A. (2008). Inclusive planning and development in the national capital territory of Delhi. ITPI Journal, 5(4), 12e20. Kundu, A. (2004). Provision of tenurial security for the urban poor in Delhi: recent trends and future perspectives. Habitat International, 28(2), 259e274. Malpezzi, S., & Mayo, S. (1987). The demand for housing in developing countries: empirical estimates from household data. Economic Development and Cultural Change, 35, 687e721. Malpezzi, S., & Tewari, V. (1991). Cost and benefit of residential rent control in Bangalore, India. World Bank. Infrastructure and Urban Development Department Discussion Paper 82. Malpezzi, S. (1999). Economic analysis of housing markets in developing and transition economies. In. Handbook of regional and urban economics, Vol. III (pp. 1791e1864). Malpezzi, S. (2001). Hedonic pricing models: a selective and applied review. Journal of Housing Economics, 10, 72e108. Mayo, S. (1981). Theory and estimation in the economics of housing demand. Journal of Urban Economics, 10, 95e116. Mehta, M., & Mehta, D. (1989). Metropolitan housing market: A study of Ahmedabad. Sage Publications. NSSO. (2004). Housing condition in India, 2002: Housing stock and constructions. NSS Report No. 488. N. S. S. Organization, Ministry of Statistics and Programme Implementation, Government of India. Pugh, C. (1991). Housing and land policies in Delhi. Journal of Urban Affairs, 13(3), 367e382. Sivam, A. (2003). Housing supply in Delhi. Cities, 20(2), 135e141. Sivaramakrishnan, K. C., Kundu, A., & Singh, B. N. (2005). Handbook of urbanization in India. New Delhi: Oxford University. Srirangan, K. (2000). Public land and property development and cross-subsidisation for low-income housing in Delhi. In Delhi field studies and workshop: Guide to good practice in core area development, DFID Research Project R6860. Times of India (TOI). (5April, 2011). Delhi’s population grows slowest in 100 yrs. Times of India. Tiwari, P., & Parikh, J. (1997). Demand for housing in the Bombay metropolitan region. Journal of Policy Modeling, 19(3), 295e321. Tiwari, P., & Parikh, J. (1998). Affordability, housing demand and housing policy in urban India. Urban Studies, 35(11), 2111e2119. Tiwari, P., Parikh, K., & Parikh, J. (1999). Effective housing demand in Mumbai (Bombay) metropolitan region. Urban Studies, 36(10), 1783e1809. UN-Habitat. (2003). The challenge of slumseGlobal report on human settlements 2003. London: Earthscan Publications. UN-Habitat. (2008). State of the World’s cities 2008/2009: Harmonious cities. Earthscan. XVI. Zacharias, A., & Vakulabharanam, V. (2011). Caste stratification and wealth inequality in India. World Development, 39(10), 1820e1833.