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Explaining Residential Ethnic Segregation in the Netherlands using Price Hedonics Cheng Boon Ong

Explaining Residential Ethnic Segregation in the Netherlands using Price Hedonics Cheng Boon Ong HSA-ECS Workshop 15 April 2010. Presentation Outline. Context Price Hedonic Method Data and specification 1 st stage semiparametric

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Explaining Residential Ethnic Segregation in the Netherlands using Price Hedonics Cheng Boon Ong

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  1. Explaining Residential Ethnic Segregation in the Netherlands using Price Hedonics Cheng Boon Ong HSA-ECS Workshop 15 April 2010

  2. Presentation Outline • Context • Price Hedonic Method • Data and specification • 1st stage semiparametric • 3rd stage: heterogeneous preference for neighbourhood ethnic composition

  3. Ethnic segregation in a Dutch context

  4. Why homeowners and segregation? • Non-price mechanism drives social rented sector (e.g. waiting list) • Increase in homeowner sector at the expense of social rented sector (mortage tax relief, privatisation of housing associations, …) • This line of research is relatively unexplored for the Netherlands

  5. Price Hedonic Method • Real estate valuation, transaction data • Microeconomic consumer choice theory (utility, budget constraint, …) • Housing as a bundle of separable attributes with unique subutility components and implicit prices for each attribute (Lancaster 1966, Rosen 1974) • Bajari and Kahn (2005): heterogeneous preferences

  6. Bajari and Kahn (2005) three-stage • 1st stage: estimate implicit prices for each housing attribute (semiparametric GAM) • 2nd stage: recover household preference parameter with 1st stage coefficients and observed housing attributes • 3rd stage: estimate joint distribution of preferences and household characteristics

  7. Utility of household i consuming dwelling j with housing attributes, k: uij = u(xj, ξj, c) = βi,kln(xj) + βi,kxj + βi,jln(ξj) + c Household preference for attribute k, βi,k = xj*,k(∂p(xj*, ξj*)/∂xj,k) …as a function of household characteristics, z βi,k = fk(zi) + εi,k

  8. Dutch Housing Survey (WoON 2006) • Nationally representative sample • > 60,000 respondents • Household characteristics, housing and neighbourhood conditions, mobility • Linked to administrative (postcode) neighbourhood data

  9. 1st semiparametric model Log(Pricej) = β0,j + β1,j*YEAR1945-1959 + β2,j*YEAR1960-1969 + β3,j*YEAR1970-1979 + β4,j*YEAR1980-1989 + β5,j*YEAR1990-1999 + β6,j*YEARafter2000 + β7,j*s(log(ROOMS)) + β8,j*s(log(INDOORSIZE)) + β9, j*s(log(OUTDOORSIZE)) + β10, j*ONEFLOOR + β11, j*GARDEN + β12, j*BALCONY + β13, j*CARPARK + β14, j*CENTRALHEAT + β15, j*DETACHED + β16, j*SEMIDETACH + β17, j*APARTMENT + β18, j*DISTANCETOWN15minwalk + β19, j* DISTANCETOWNwithintown + β20, j*DISTANCETOWNsurrounding + β21, j*DISTANCETOWNcountryside + β22, j*s(log(MEANINCOME)) + β23, j*s(log(NONWESTERN)) + β24, j*s(log(URBANITY)) + β25, j*BIGCITY

  10. ‘Downhill’ once proportion of non-western minorities exceeds 3.7%

  11. Partial residual plots show nonlinearity

  12. 3rd stage: OLS MWTPi,nonwestern= β0,i + β1,iFamilyKid + β2,iHouseholdSize + β3,iNativeDutchHead + β4,iWesternHead + β5,iNonWesternPartner + β6,iLowIncome + β7,i1to1.5ModalIncome + β8,i1.5to2ModalIncome + β9,i2ModalhighIncome + β10,ilog(Age) + β11,iTertiaryEducated

  13. Calculate Marginal Willingness to Pay MWTPnonwestern10-35%increase,i = βi,k*(10) - βi,k*(35) MWTPnonwestern0-3%increase,i = βi,k*(3) - βi,k*(0)

  14. Some preliminary conclusions • Nonlinear relationship between proportion of non-western households in neighbourhood and dwelling price • Different demand across ethnicity of household for non-western neighbours – some positive “taste” for non-western neighbours up to a certain level and then the “distaste” sets in

  15. Thank you for your attention! Comments/suggestions welcome: cheng.ong@maastrichtuniversity.nl

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