Valuation of Urban Environmental Amenities in Mumbai and its

Suburbs: A Hedonic Pricing Approach

Vijaya Gupta*, G Mythili**, D S Hegde*** and Rajesh Patil****

Abstract Estimates of residents’ valuation attached to urban environmental amenities are needed to assess the ever increasing scarcity of these parameters in a highly congested city like Mumbai with a population of 14 million and density of 27,209 persons per square kilometer. The city continues to attract investors, expatriates and migrants and demand for “green residence” is outstripping its supply. The main objective of this study is to value the urban environmental amenities in Mumbai. Though there is no direct market for these attributes, it can be indirectly captured from the property (real estate) prices. This is done by gauging the impact of environmental, structural and location variables on housing price prevailing in different parts of the city. The areas chosen for the study are central, north and Navi Mumbai. We have collected primary cross-sectional data during July-November 2006 from 578 new house owners from different locations in Mumbai such as Chembur, Yari road, Vashi, Mahim, Dadar, Sion, Prabhadevi, Vikroli, Powai, Andheri, Thane, Lalbaug, Kalwa, Sanpada, Kandivali, Nerul, Deonar, Wadala, Byculla, Santacruz, Versova, Malad Tilak Nagar, Belapur, Hiranandani, Virar, Parel and Borivali. These locations are grouped as Northern Suburbs, Central Suburbs and Navi Mumbai. We have considered physical and visual access to water body, greenery, noise and slums as environmental parameters; physical access to the market, office, school, bus and railway stations as location parameters; and quality of construction, interiors on offer, and other amenities as structural parameters. We use hedonic price function to capture the impact of environmental, location and structural attributes on the price per square foot (PSF), prevalent in Mumbai. In the estimation procedure, the problems of multicollinearity and the cross-sectional error related problem of heteroscedasticity are anticipated. Presence of multicollinearity or heteroscedasticity warrants appropriate techniques to derive the reliable estimates. Attempt has been made to correct the problem before the parameters are estimated. Analyses using both linear and log-linear specifications are performed. The marginal value of each environmental, structural and location indicators are determined using the estimated coefficients of hedonic price regression. Keywords Hedonic pricing, environmental amenities, Mumbai

*Associate Professor at NITIE, Mumbai, vijaya298@yahoo.com **Associate Professor at Indira Gandhi Institute of Development Research (IGIDR), Mumbai, ***Professor at NITIE, Mumbai, ****Student of Post Graduate Diploma in Industrial Engineering, National Institute of Industrial Engineering (NITIE), Vihar Lake, Mumbai-400087

1. Introduction Residential house is the most expensive, multidimensional commodity which is durable, and structurally and spatially inflexible. Each residential unit is having a different bundle of attributes including environmental, location, and structural etc. parameters. The urban environmental amenities are defined as all those natural assets including green spaces that are aesthetic, ecological, and economic in nature, as well as those that have a physical or psychological effect on human health, such as pollution control, noise abatement, and the provision of recreational opportunities (Tyrväinen and Miettinen, 2000). This study attempts to understand that how Mumbai residents perceive scarcity of environmental amenities and are willing to pay additional to receive associated benefits from the perspectives of their homes and the surrounding environment. We use Hedonic Price Function to analyze primary data gathered from 578 housing market transactions in northern suburbs, central suburbs and Navi Mumbai during July-November 2006. This paper has been arranged in six sections. The following Section gives an overview of Mumbai city, its housing problems and policy and natural assets briefly. Section 3 gives an account of Hedonic Price Method, related literature and empirical studies. Section 4 on Data and Methodology discusses study area, sample size, variables included and the method. Section 5 analyses results and the paper concludes with Section 6 by providing concluding remarks.

2. Mumbai city

2.1 An Overview

Mumbai is the cultural and financial capital of India, spreading over 603 square kilometers, and made up of seven islands in city and 4 islands in suburbs. The population which was 11,914,398 in 2001 (Census 2001), increased to 19,944,372 in 2006 with density of 27,220/km² due to agglomeration (BMC, 2007). Its suburban rail system called “lifeline of Mumbai” carries a total of 2.2 billion passengers every year. Mumbai is one of the most expensive places in the world to buy a condominium unit after London, New York, Moscow, Hong Kong, Tokyo and Singapore (Cruz, 2007). In India, cities are struggling to accommodate the influx of people from rural areas without proportionate rise in public and social infrastructure. Demand for quality accommodation of expatriates and mass affluent individuals outstrip supply and making some localities much sought after and expensive due to their environmental, location and structural amenities. Mumbai needs to overcome a significant number of problems related to transportation, water supply, flooding, water and air pollution, illegal occupation in shanty hutments, ethnic tensions etc. to attain the status of modern world city (Pacione, 2006). In Mumbai, approximately half of the population lives in slums and occupies only 8% of land (UNCHS, 2001). It has a very low ratio of green spaces per inhabitant. As a result, the

clash between “brown agenda” and “green agenda” is even more exacerbated than in most other developing cities (Zérah, 2007).

2.2. Mumbai’s natural assets or liabilities

Mumbai is located in 10-15 meters of sea level. The coastline is indented with numerous creeks and bays and the eastern coast is covered with large mangrove swamps and rich in biodiversity. Five lakes viz. Vihar, Vaitarna, Tulsi, Powai and Tansa are located here. Tulsi and Vihar Lakes are located in Borivali or Sanjay Gandhi National Park, and supply drinking water to part of the city. The city has four rivers–the Oshiwara, Dahisar, Poisar, and Mithi, of 10-15 kilometers, all originating in the Borivali National Park and ending into the Arabian Sea. These rivers are currently highly polluted and narrowed down due to illegal dumping of industrial effluents, sludge oil, garbage, animal waste, municipal waste, raw sewage of slums, debris, plastic bags etc. Their importance as storm water drains during monsoons has reduced and on the contrary, they pose as a threat during high tide bringing polluted water back into the city. Sanjay Gandhi National Park or Borivali National Park spreads over 9000 hectares, preserves and nurtures the varied flora and fauna and wild animals of the area. It has gradually begun to become denuded and barren due to unauthorized wood cutting, encroachments of slums and urban developmental activities.

2.3. Mumbai housing policy There are number of acts and regulations to control housing market in Mumbai. To name a few, Maharashtra Ownership of Flats Act (MOFA) 1963, Development Control Regulation (DCR) 1999, Transfer of Development Right (TDR) as part of DCR 1991 for the purpose of creating public amenities, Urban Land Ceiling Regulation Act (ULCRA) 1976 for preventing individual land holding more than 500 square meters, Coastal Regulation Zone (CRZ) preventing construction activity within 500 meters of high tide line area, Act II 2008 for unlawful construction, Housing Regulatory Commission to regulate the prices of low and middle income group houses, White Paper to generate affordable housing stock, and a comprehensive and transparent action plan for redevelopment of slums etc. Irrespective of these regulations in place, this market suffers from irregularities due to misinterpretation, misuse, and or noncompliance of these rules and regulations. The Municipal Corporation of Greater Mumbai (MCGM) is launching the eco-housing program to encourage environment-sensitive residential development in the city. An effective monitoring and verification mechanism for certification of eco-housing projects are expected to encourage adoption of energy efficient and resource conservation measures during the developmental process.

3. Hedonic Price Method and Literature (still working on it)

The Hedonic Price Method (HPM), one of the revealed preference approaches has drawn its basis from the Lancaster’s theory of attributes where consumer derives utility from the properties or characteristics of the good. The consumption is an activity in which goods

are inputs and collections of characteristics are outputs and these characteristics give rise to utility to a consumer (Lancaster, 1966). Environmental amenities in an urban setting can be valued by means of attaching them to a residential property and when a buyer purchases a house, the amenities are part of the bundle that a household purchases. The essence of the hedonic approach lies in decomposing the price of a house P into prices of individual attributes(X1, X2, ……Xn) including environmental amenities. This can be achieved using a hedonic price function which specifies an equilibrium relationship between the price and the number of

attributes. The marginal price of an attribute isiX

P∂∂ Consumers equate their marginal

willingness to pay for each attribute to its marginal price when making the final choice (Rosen, 1974). However hedonic price function can imply only marginal changes in the environmental attribute. If there are large changes, i.e. non-marginal changes in the environmental quality, the derivative of the price function no longer measures the value of the amenity change. Similar houses may be valued differently by different home buyers in heterogeneous housing market as they posses unique utility function causing them to value characteristics differently (Sirmans et al., 2005). This valuation is done through hedonic prices which are defined as implicit prices of these utility-bearing attributes and are revealed to property dealers or agents. The proximate principle suggests that the value of an amenity is partially captured in the price of residential properties proximate to it (Crompton, 2001). Home locations near to open spaces such as greenways are considered desirable and home buyers are willing to pay extra dollar to own a residence and have visual and physical access, represents capitalization of the land into proximate property values (Nicholls and Crompton, 2005). At the first stage of property value technique, implicit prices for different housing characteristics are estimated. These implicit prices can directly be used to evaluate the benefit or losses arising from marginal changes in the environmental parameters (Tyrväinen and Miettinen, 2000). Tyrväinen and Miettinen, 2000, valued implicitly non-priced urban forest amenities by comparing dwelling prices and specific amounts of amenities associated with the dwelling units. Using linear Box-Cox model on the sales data of terraced houses in the district of Salo in Finland, they estimated that a one kilometer increase in the distance to the nearest forested area leads to an average 5.9 percent decrease in the market price of the dwelling. Dwellings with a view onto forests are on average 4.9 percent more expensive than dwellings with otherwise similar characteristics.

4. The Data and Methodology We have chosen Mumbai city for this empirical study due to high level of infiltration of people, and so high demand for housing outstripping its supply resulting in high prices of

houses along with increasing scarcity of environmental quality. The home buyers reveal their preferences explicitly through their willingness to pay in terms of Rupees for per square foot (as the transaction takes place in Mumbai) for implicit values of the attributes of house. We have captured 578 market transactions for 1-3 bedrooms, hall and kitchen (BHK) multi storied flats during July-November 2006. As per Maharashtra Owner Ship Flat Act, 1963, a flat means a self-contained premise used or entitled to be used for the purpose of residence. Since housing prices have risen sharply in Mumbai and its suburbs due to scarcity of land and increasing levels of economic activities, we have restricted our study to a shorter duration to avoid the influence of price fluctuations on our study. We undertook a pilot study to understand the variables that affect home buyer’s choice through which, we identify visual and physical access to garden or greenery, water bodies (lake or pond), noise and slums as environmental parameters, distance from the market, office, bus transport, schools, and railway station as location parameters, and quality of construction, interiors on offer, and other amenities are being identified as structural parameters. For the purpose of the study the price per square foot has been considered as the dependent variable and the other factors as independent variables. The primary data of sample size 578 has been collected from major locations in Mumbai, its suburbs and neighboring areas well connected to the city. The locations from where data has been collected are: Chembur, Yari Road, Vashi, Mahim, Dadar, Sion, Prabhadevi, Vikroli, Powai, Andheri, Thane, Lalbaug, Kalva, Sanpada, Kandivali, Nerul, Deonar, Wadala, Byculla, Santacruz, Versova, Malad Tilak Nagar, Belapur, Hiranandani, Virar, Parel, and Borivali. These locations are grouped as Northern Suburbs, Central Suburbs and Navi Mumbai for data analysis, results and discussion purposes. We have not included South Mumbai in this study due to different characteristics of the housing market in the area leading to exceptionally high housing prices. The explanatory variables and their expected effects are presented in Table 1. Environmental variables –

1. Water body – physical and or visual access to any water body such as swimming pool, pond, lake, river, beaches and sea (These environmental amenities are preferred for recreational, aesthetic and ecological functions also they further improve the urban landscape. Physical access to environmental amenity provides direct consumptive or non-consumptive use value whereas visual access may provide indirect consumptive or non-consumptive use value.

2. Greenery - physical and or visual access to parks, gardens, forests etc 3. Noise – exposure to any kind of noise 4. Slums – as 60-70% of the population of Mumbai lives in highly congested and

dingy slums, we have considered physical and or visual access to these slums as negative environmental parameter.

Location parameters 5-8 proximity to market, work place, school, bus station, railway station has been

considered as location parameters. Proximity to these places provides convenience, saves and protects from stressed and noisy traffic pollution. But

proximate residential places themselves become more noisy, polluted and congested.

Structural parameters 9-10 the offer of the quality of construction, interiors, and additional recreational

amenities in the building such as swimming pool, garden, park, mini theater, gym, common room etc in the housing area.

Table 1 The Explanatory Variables and their Expected Effects on the Price of House Parameters

Explanatory variables

Scale definition

Expected effect

Water body

No physical and visual access to high physical and visual access to water body in the scale of 1-5

+

Greenery No physical and visual access to high physical and visual access to greenery in the scale of 1-5

+

Noise High level of noise to no noise in the scale of 1-5

_

Environmental

Slums Physical and visual access to slums yes-0, no-1

_

Proximity to market

Actual Distance in kilometers _

Proximity to school

Actual Distance in kilometers _

Proximity to work place

Actual Distance in kilometers _

Location

Proximity to nearest railway station

Actual Distance in kilometers _

Quality of construction and interiors offered

Low end to high end in the scale of 1-3 + Structural

Other amenities in the building

Basic to high in the scale of 1-3 +

+ Increasing / - reducing effect on the selling price

The present study is unable to incorporate the age of the building, neighborhood characteristics that affect selling prices, such as crime rates, and quality of schools, and also renovation, property taxes etc. There exists some scope to refine this study by incorporating some more relevant variables.

Hedonic price function is specified as uZXP +++= γβα Where X’s are environment related variables such as proximity to water body, proximity to greenery and noise exposure.

iX

P∂∂ = β i

β ’s indicate environmental values. Zs are other variables related to infrastructure and house characteristics such as distance from educational institutes, office, bus facility and railway station, and proximity to slum. The environment related variables are categorical variables with data taking values 1 or zero depending upon whether proximity to environmental attributes are either yes or no. The error terms are tested for presence of heteroscedasticity and adjustment has been made wherever needed.

5. Results and discussions Three alternative specifications were attempted: linear, semi log and log-log form. In semi log form, only the dependent variable is considered in logarithms. In log-log form both the dependent and the quantitative independent variables were considered in logarithms. All the environmental variables are categorical variables and hence considered in linear form as dummy variables. Linear form coefficients directly give values of the environmental variables, whereas in semi log or log-log form, the values are obtained as follows: Values= β * average (P). The dummy variables are constructed as follows: Proximity to water body: categorical variables – Value range= 1 to 5 Classified as (no or low proximity=0) (medium or high proximity=1) Proximity to garden: categorical variable- value range =1 to 5 Classified as (no or low proximity=0) (medium or high proximity =1) Exposure to traffic noise: categorical variable- value range=1 to 5 Classified as (high or medium noise=0) (low or no noise=1) Interiors offered: categorical variable- value range=1 to 3 Classified as (low end=0) (medium or high end=1) Other facilities offered: categorical variable- value range=1 to 3 Classified as (basic=0) (medium or high=1) Proximity to slum: categorical variable- value range=0, 1 Classified as (proximity=0) (no proximity=1)

Other variables such as distance to office, school, market, railway station are considered in kilometers. Table 2. Mean values of the variables Variables Northern

Suburb Central suburb

Navi Mumbai

Price Rupees per square foot 2800 6127 2658 Log (price per square foot) 7.89 8.67 7.82 Water access 0.439 0.548 0.346 Greenery access 0.512 0.748 0.423 Noise exposure 0.573 0.793 0.365 Slum proximity 0.390 0.812 0.250 Distance to School(km) 1.617 1.126 1.713 Log (distance to school in m.) 7.307 6.780 7.335 Distance to office (km) 16.85 5.86 14.20 Log (distance to office in m.) 9.55 8.49 9.45 Distance to rail. Station (km) 1.16 2.066 1.158 Log (distance to railway statn in m.) 6.91 7.144 6.917 Distance to market (km) 0.951 0.669 1.396 Log (distance to market in m.) 6.703 6.316 7.160 Interior 0.512 0.974 0.577 Other facilities 0.073 0.652 0.057

Table 3. Estimation of Environmental, Location and Structural Attributes’ Values in Linear Version (Dependent variable= Price per square foot in Rupees ) Variables Northern

Suburb Central suburb

Navi Mumbai

Water proximity 168.28* 549.84* 332.33* Garden proximity 278.03* 489.45* 270.04* Noise exposure -81.65* 905.38* 123.87* Slum proximity 568.96* 216.50* 733.62* Distance to School(km) -132.86* -447.51* -82.50* Distance to office (km) -11.15* -5.49 Distance to rail. Station (km) 174.28* -56.19* 146.09* Distance to market (km) -552.26* Interior 635.88* 1007.13* 537.94* Other facilities 1757.85* Adj R2 0.92 0.93 0.93 F statistic 259.85* 459.99* 182.01* Sample size 164 310 104

* Significant at 5% level.

All the three specifications provided high R2 values, ranging from 0.92 to 0.95, indicating good explanatory power of the model. In the linear form, the coefficients directly give the values for the environmental attributes. For instance, as compared to the houses with no proximity to water body houses with proximity fetch Rs. 168 more value per square foot of the house in Northern Mumbai. The respective values for Central suburb and Navi Mumbai are 550 Rs. and 332 Rs. Garden proximity and being away from noise pollution seem to be fetching more values in the Central suburb of the three localities whereas proximity to water body obtain highest value in the Navi Mumbai region. Regarding infrastructure, Schools and Office location have significant positive impact on house price. For instance, schools located away from the house, every one kilometer additional distance decreases the value by Rs. 132 per square foot. No proximity to slum has the highest positive impact in Navi Mumbai among the three localities. Proximity to railway station is a favourable factor only in the case of low and medium priced residential locations and in Central Mumbai, where property prices are high, and proximity to railway station acts as a negative factor. People in the higher income groups may not prefer to stay nearer to the railway station as this affects the peaceful environment. Market proximity is not a significant factor influencing the choice of a house in the northern and central suburb whereas in Navi Mumbai, it is influencing negatively. Table 4. Estimation of Environmental, Location and Structural Attributes’ Values in Semi Log Version (Dependent variable= Logarithm of Price per square foot in Rupees) Variables Northern

Suburb Central suburb

Navi Mumbai

Water proximity 0.0872* 0.1079* 0.1656* Garden proximity 0.0875* 0.1214* 0.0958* Noise exposure -0.0278* 0.1036* 0.0400* Slum proximity 0.1518* 0.0420* 0.1810* Distance to School(km) -0.070* -0.0551* -0.0992* Distance to office (km) -0.004* -0.0010* Distance to rail. Station (km) 0.098* -0.0086* 0.0469* Distance to market (km) -0.1752* Interior 0.2510* 0.1332* 0.2252* Other facilities 0.3519* Constant 7.7087* 8.2544* 7.8896* Adj R2 0.928 0.95 0.9469* F statistic 262.81* 575.32* 230.82* Sample size 164 310 104

* Significant at 5% level. In both the Semi-log and the Log-log versions, the environmental variables are in dummy form and hence the interpretations are same. The two versions differ only in the

infrastructural variables like school, office and market distances which are continuous variables. Table 5. Estimation of Environmental, Location and Structural Attributes’ Values in Log – log version (Dependent variable= Logarithm of Price per square foot in Rupees All the quantitative independent variables are in logarithms).

Variables Northern Suburb

Central suburb Navi Mumbai

Water proximity 0.1300* 0.1108* 0.1886* Garden proximity 0.0712* 0.1326* 0.0927* Noise exposure -0.0153* 0.0976* 0.0618* Slum proximity 0.1516* 0.0435* 0.1365* Distance to School(km) -0.1189* -0.0456* -0.1918* Distance to office (km) -0.0298* -0.0234* Distance to rail. Station (km) 0.1082* 0.0108* 0.0860* Distance to market (km) -0.1798* Interior 0.2508* 0.0884* 0.2610* Other facilities 0.3421* Constant 8.0235* 8.6138* 9.6047* Adj R2 0.929 0.942 0.935 F statistic 268.79* 559.96* 188.88 Sample size 164 310 104

* Significant at 5% level.

In the log-log version, the values are interpreted in percentage terms (Table 5). For instance, proximity to water body fetches 13% more value. Similarly garden proximity fetches 7% more value. The percentages are transformed in values and the estimates are presented in the table for all specifications. Table 6. Environmental Valuation – Marginal WTP (Rs.) Per Square Foot Variables/ specification Northern

suburb Central suburb Navi Mumbai

Water body-linear Semi log Log-log

168 244 364

549 661 679

332 440 501

Greenery- Linear Semi log Log-log

278 245 200

489 744 812

270 255 256

Noise pollution free Linear Semi log Log-log

-82 -77 -42

905 634 598

123 106 164

From Table 6, it is inferred that preference to location which is free from noise pollution, fetches the most value in central suburb and it is the least important factors in both Northern Suburb and Navi Mumbai. Water body is the most preferred environmental attribute in Navi Mumbai as the area is less endowed with ‘water bodies’ and hence marginal value is higher. Greenery is an important attribute in Northern Suburb going by all the three specification of the model. As regards absolute marginal value, all the three attributes fetch highest values in Central suburb. The reason for this is, central suburb is a premium location in Mumbai and the base prices are higher. Hence if we compute values as percentages of total prices of a house then Navi Mumbai fetches higher amenity values for both ‘Greenery’ and ‘water body’ resources whereas Central suburb fetches more value for ‘noise pollution free’ environment.

6. Conclusions

Mumbai, one of the most populous and expensive cities of the world, is struggling to accommodate the influx of people from rural areas without proportionate rise in public and social infrastructure. The demand for quality accommodation is outstripping its supply, leading to environmental, location and structural amenities’ scarcity. This study tries to understand Mumbai residents’ perceived value for scarcity of environmental amenities and their marginal willingness to pay to receive associated benefits from the perspectives of their homes and the surrounding environment. To achieve this objective, we used linear, semi-log and log-log versions of Hedonic Price Function to analyze primary data gathered from 578 housing market transactions in Northern Suburbs, Central Suburbs and Navi Mumbai during July-November 2006. All the three specifications provided high R2 values, ranging from 0.92 to 0.95, indicating good explanatory power of the models. In the linear form, houses proximate to water body fetch Rs. 168 more value per square foot as compared to a house away from water body in Northern Mumbai, Rs 550 in Central suburb and Rs 332 in Navi Mumbai. We also found significant positive impact of schools and office locations on house price whereas proximity to railway station is favored only in case of low and medium priced residential locations. In the log-log version, proximity to water body fetches 13% more value, and garden proximity fetches 7% more value. It was found that preference to noise free location fetches the most value in Central Suburb and least in both Northern Suburb and Navi Mumbai. Water body is the most preferred environmental attribute in Navi Mumbai and greenery is an important attribute in Northern Suburb. As regards absolute marginal value, all the three attributes gets highest values in Central Suburb reflecting high scarcity of environmental quality in the area. However if we consider percentage of amenity values to total price of the property, then Navi Mumbai fetches the highest percentage of value for both ‘greenery’ and ‘water body’ amenities indicating increasing demand and it is to be mentioned that this location is recently witnessing fast growth in the residential property.

The results of this study may prove useful to the urban planners in their planning.

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Map 1 Location in Mumbai

Photo 1 Coexistence of Shanty Hutments and Multistoried Buildings in Mumbai