1# RECORD 291ca9688b933cfaf7d01e6f6552e428bb2432b52# TITLE: Occupant Well-Being and House Values3# YEAR: 2018.0 VENUE: nan DOI: 10.36934/wecon2019-0234# AUTHORS: R. Rijnks; S. Sheppard5# PAGES: 38 CANDIDATE TABLE PAGES (1-based): [17, 23, 24, 25, 26]6# ABSTRACT: A difficulty in identifying the contribution of structure and neighborhood attributes to the market value of residential property is the lack of data on subjective characteristics of the neighborhood (friendliness of neighbors, proximity to friends and acquaintances) or difficult-to-observe subjective attributes of the structure itself (such as "curb appeal" or the presence of unpleasant odors). Concern may also arise from the understanding that the observed market price of most residential property is the result of a process of bargaining. A buyer who is optimistic by nature may assume that the quality of the neighborhood will be wonderful, or that the unusual odor will eventually go away, and therefore be willing to bid a higher price for the structure than a prospective buyer who is more nervous about all the ways that a house purchase can generate disappointment. Estimates of the value of structure or neighborhood attributes may tell us as much about the emotional affect of the buyer as they do about the actual costs or benefits of the attributes (or of cleaning or mitigating them). These observations suggest that incorporating data on the levels of subjective well being (SWB) and emotional affect of the buyers might be usefully applied to improve hedonic analysis of housing markets. The goal of this paper is to undertake such analysis and to explore the potential for improved analysis of the value of residential property. We make use of unique data collected as part of a multi-year analysis of health outcomes, matched with data on market transactions of residential property in three provinces of the Netherlands. We employ the spatial model developed by Kelejian and Prucha (2010) which allows us to incorporate a spatial error specification, as well explicitly control for possible endogeneity between the measure of SWB and the transaction price. By examining aggregate measures of SWB at different spatial scales, we obtain insights into whether these measurements are capturing subjective characteristics of the community, the neighborhood or the structure and the buyer who negotiateover the eventual price.789===== PAGE 1 (score 8, numeric-paren 0, table-words 1, var-words 6) =====1011Occupant Well-Being and House Values †12Richard H. Rijnks‡†13Stephen Sheppard §‡14August 29, 202015Abstract16Recent research indicates that subjective evaluation of well-being increases when conditions of housing are improved.17This suggests that subjective well-being might serve as a useful proxy for characteristics of a home or neighbourhood18that are relevant to an occupant, but unobserved by the analyst. In this paper, we assess this idea through analysis19of residential property valuation, using a sample of 95,413 respondents matched to house sales for 2000 to 2012 in the20North of the Netherlands. Using a spatial econometric approach, we find a significant and positive association between21individual and regional subjective well-being and house prices. This suggests that house buyers are willing to pay more22for, or that house sellers require greater compensation to sell and move from, properties and areas in which the resident23experiences greater happiness. Our study provides the first estimates of the importance of these effects.24Keywords: Subjective well-being, house price, hedonic model25JEL Classification: D91, R2, R326Author(s) Conflict-of-interest: None27†We would like to thank Erik Johnson for helpful comments, and to acknowledge funding support from the University of Groningen, Campus28Fryslân, and the Waddenacademie29‡Department of Economic Geography, University of Groningen, Groningen, Netherlands30§Department of Economics, Williams College, Williamstown, Massachusetts313233===== PAGE 17 (score 49, numeric-paren 0, table-words 13, var-words 18) =====3435Table 3: Descriptive statistics for model variables36Variable name37Mean38Std Dev39Min40Max41Property sales price42200,3114396,523441850145245000046Ln(sales price)4712.118480.412499.8265014.71251Ln(positive affect)521.511530.095540.693551.79256Ln(well-being+1)574.366580.207590604.61561Ln(lag well-being+1)624.381630.057643.219654.61566Ln(urban distance)672.443681.15469-4.216703.86271Ln(density)725.904730.942744.007757.79776Ln(foreign+1)771.633780.469790.693802.48581Ln(burglary+1)823.294830.508841.902854.05586Ln(floor area+1)874.813880.290893.555906.22391Ln(lotsize+1)†925.399931.7429409511.19596Ln(rooms)971.541980.2609901003.526101Dichotomous Variables102Two Bathrooms1030.0561040.001910501061107Three Bathrooms1080.0011090.000211001111112Balcony1130.1261140.33211501161117Parking1180.6051190.48912001211122Basement1230.9361240.24512501261127Attic1280.3441290.47513001311132Under Roof Area1330.1431340.35013501361137Listed1380.0041390.06514001411142Monument1430.0071440.08214501461147The population density of the municipality containing the house is included as an additional control for effects of smaller148population centers (rural towns and large villages).149The official classification of “Non-western foreigners” in Dutch statistics is people who have a migration history (their150own or at least one of their parents) from Africa, Latin-America, Asia, or Turkey, excluding Indonesia and Japan, as described151in Dutch National Statistics (CBS) (2018a). We include 1 plus the percentage of population with a non-western background152in the municipality containing the house as a proxy for ethnic diversity in the neighbourhood.153From Table 4 we see that just over 60% of the properties in the sample were constructed in the 1970’s or later. The154median sales date of properties in the sample is mid-2006.155Operationalisation of main variables156The use of subjective measures of happiness has seen tremendous progress over recent decades, although the field has yet to157reach consensus on which measures are preferable, as noted by Frey and Stutzer (2002). Generally, three distinct constructs158of happiness are evaluated which capture separate but complementary information on overall subjective well-being. The first159component of overall subjective well-being is the general happiness or general life satisfaction component. This is measured160either through a single survey item (e.g. all things considered, how satisfied are you with your life these days?) or preferably16116162163164===== PAGE 18 (score 7, numeric-paren 0, table-words 1, var-words 1) =====165166Table 4: Descriptive statistics for indicator variables167Variable168Mean169Variable170Mean171Interior maintenance172Exterior maintenance173Interior 1 (Poor)1740.10175Exterior 1 (Poor)1760.11177Interior 21780.07179Exterior 21800.02181Interior 31820.66183Exterior 31840.48185Interior 41860.24187Exterior 41880.26189Interior 51905.18191Exterior 51924.84193Interior 61942.79195Exterior 61963.06197Interior 719877.24199Exterior 720078.33201Interior 82021.47203Exterior 82041.41205Interior 9 (Excellent)20612.25207Exterior 9 (Excellent)20811.50209Construction period210Year property sold211Up to 19052124.35213Y20002145.472151906-19302169.65217Y20012186.912191931-19442207.39221Y20022227.982231945-19592245.18225Y20032268.052271960-197022812.98229Y20042308.792311971-198023222.51233Y200523410.242351981-199023613.81237Y200623811.052391991-200024018.82241Y20072429.512432000 and later2445.31245Y20082468.64247Y20092486.95249Insulation250Y20102516.43252No insulation2539.99254Y20112555.532561 type of insulation25732.52258Y20122594.452602 types of insulation26113.582623 types of insulation26312.482644 types of insulation26510.94266Fully insulated26720.50268a compound measure using a set of questions as suggested in Kahneman and Krueger (2006). The general happiness or269general life satisfaction component is used to measure a cognitive evaluation of the quality of life by the respondent.270The measurement used for this study is drawn from the SF-36 item short form survey. The SF-36 survey is one of the271most widely used approaches to measuring health related quality of life, and is surveyed in Hays and Morales (2001). The272survey groups 36 items into eight separate constructs. Seven of these deal with a variety of issues relating to physical health,273subjective health, social functioning, and experienced limitations and pain. The construct from SF-36 we use in this study is274Emotional well-being. Emotional well-being is measured on a 0 to 100 scale by weighting and adding together the underlying275items, which are the extent an individual felt happy, nervous, depressed, calm, and downhearted over the past four weeks.276The responses then averaged for each individual. The measure is tested for internal reliability (Cronbach’s Alpha = 0.83),277showing that the underlying items are reliably correlated. Spatial lags of this variable were considered at 1, 2.5, 5 and 10278kilometers, using an inverse euclidean distance weighting.27917280281282===== PAGE 23 (score 24, numeric-paren 9, table-words 6, var-words 0) =====283284Table 6: Subjective well-being and time in residence285Coefficients:286Years287Days288Constant2892.8497***2902.8530***291σ2920.05222930.0547294Ln(Affect + 1)2950.7642***2960.7710***297σ2980.01672990.0173300Ln(Price)3010.0294***3020.0284***303σ3040.00393050.0041306TiR307−1.4867 × 10−3308−5.5870 × 10−6309σ3101.5609 × 10−33115.7080 × 10−6312TiR23133.5530 × 10−4**3143.0070 × 10−9**315σ3161.4390 × 10−43171.3180 × 10−9318F(4, 14852)319562.7***320527.6***321RSE3220.19313230.1943324R23250.13163260.1943327¯R23280.13143290.1324330*** - p < 0.001, ** - p<0.01, * - p<0.05, † - p<0.10331spatial econometric approaches to account for spatially correlated errors, spatial autoregressive structures, and spatially332lagged effects of subjective well-being. The dependent variable in all models is the log of the transaction price.33322334335336===== PAGE 24 (score 102, numeric-paren 64, table-words 9, var-words 6) =====337338Table 7: Hedonic model estimates339Variable340OLS Model 1341OLS Model 2342OLS Model 3343IV Health344IV Health/Pain345GNS346SAC347Behavioural and Subjective Characteristics348Well-being3490.0385***3500.181***3510.1744***3520.0893***3530.1149***354σ3550.0093560.0293570.0263580.0183590.023360Lag well-being (θ)3610.1007**362σ3630.035364Positive affect3650.1018***366-0.0077367-0.00273680.03213690.0094370σ3710.023720.0293730.0283740.0213750.024376ρ (spatial error dependence)3770.4282***3780.4238***379σ3800.0123810.012382λ (spatial autocorrelation)3830.2409***3840.2445***385σ3860.0123870.012388neighbourhood and Environmental Characteristics389Ln(Urban distance)390-0.0053*391-0.0055*392-0.0058*393-0.0058*394-0.0119***395-0.0116***396σ3970.0023980.0023990.0024000.0024010.0034020.003403Ln(Density)4040.1008***4050.1006***4060.1003***4070.1003***4080.0859***4090.0859***410σ4110.0054120.0054130.0054140.0054150.0074160.007417Ln(Foreign)418-0.1536***419-0.1535***420-0.1531***421-0.1531***422-0.1164***423-0.1159***424σ4250.0094260.0094270.0094280.0094290.0134300.013431Burglary432-0.0185***433-0.0182***434-0.0171**435-0.0172**436-0.0118437-0.012438σ4390.0054400.0054410.0054420.0054430.0084440.008445Ln(Water distance)446-0.0087***447-0.0087***448-0.0088***449-0.0088***450-0.0116***451-0.0115***452σ4530.0024540.0024550.0024560.0024570.0024580.002459Ln(Forest distance)460-0.0045*461-0.0043*462-0.0038*463-0.0039*464-0.0039465-0.004†466σ4670.0024680.0024690.0024700.0024710.0024720.002473Ln(Nature distance)474-0.0553***475-0.0549***476-0.0546***477-0.0547***478-0.0357***479-0.0359***480σ4810.0034820.0034830.0034840.0034850.0044860.004487Structure Characteristics488Ln(Floor Area)4890.6616***4900.6508***4910.6485***4920.6471***4930.6471***4940.5932***4950.5931***496σ4970.0094980.0094990.0095000.0095010.0095020.0085030.008504Ln(Lotsize)5050.0461***5060.0514***5070.0514***5080.0514***5090.0514***5100.0553***5110.0552***512σ5130.0015140.0015150.0015160.0015170.0015180.0015190.001520*** - p < 0.001, ** - p<0.01, * - p<0.05, † - p<0.10521Continued on next page52223523524525===== PAGE 25 (score 99, numeric-paren 72, table-words 0, var-words 12) =====526527... continued from previous page:528Variable529OLS Model 1530OLS Model 2531OLS Model 3532IV Health533IV Health/Pain534GNS535SAC536Rooms5370.0309***5380.0327***5390.0326***5400.0323***5410.0323***5420.0498***5430.05***544σ5450.0095460.0095470.0095480.0095490.0095500.0085510.008552Two Bathrooms5530.1438***5540.1395***5550.1386***5560.1374***5570.1375***5580.1164***5590.116***560σ5610.0085620.0085630.0085640.0085650.0085660.0075670.007568Three Bathrooms5690.2432***5700.2489***5710.2487***5720.2542***5730.254***5740.1801***5750.1806***576σ5770.0635780.0615790.0615800.0615810.0615820.0535830.053584Balcony5850.1196***5860.1158***5870.1153***5880.1153***5890.1153***5900.087***5910.0871***592σ5930.0065940.0065950.0065960.0065970.0065980.0055990.005600Parking6010.1244***6020.1342***6030.1338***6040.1327***6050.1327***6060.1203***6070.1205***608σ6090.0046100.0046110.0046120.0046130.0046140.0046150.004616Basement617-0.0192*618-0.0228**619-0.0227**620-0.0223**621-0.0223**622-0.032***623-0.0319***624σ6250.0086260.0086270.0086280.0086290.0086300.0076310.007632Attic633-0.0392***634-0.0371***635-0.0373***636-0.0389***637-0.0389***638-0.0316***639-0.032***640σ6410.0046420.0046430.0046440.0046450.0046460.0046470.004648Under roof649-0.0064650-0.0051651-0.0054652-0.0073653-0.0073654-0.0106*655-0.0112*656σ6570.0066580.0066590.0056600.0066610.0066620.0056630.005664Listed6650.2465***6660.2313***6670.2287***6680.2257***6690.2258***6700.1899***6710.1895***672σ6730.0296740.0286750.0286760.0286770.0286780.0256790.025680Monument6810.1416***6820.1425***6830.14***6840.1418***6850.1418***6860.1648***6870.1648***688σ6890.0246900.0236910.0236920.0236930.0236940.026950.02696Constant6978.3239***6988.5819***6998.271***7007.8333***7017.8533***7024.8285***7035.1519***704σ7050.0727060.0797070.0887080.1227090.1177100.2427110.185712Other variables in models713Int/Exterior maintenance714Yes715Yes716Yes717Yes718Yes719Yes720Yes721Insulation level722Yes723Yes724Yes725Yes726Yes727Yes728Yes729Construction Period730Yes731Yes732Yes733Yes734Yes735Yes736Yes737Year of sale738Yes739Yes740Yes741Yes742Yes743Yes744Yes745*** - p < 0.001, ** - p<0.01, * - p<0.05, † - p<0.10746Continued on next page74724748749750===== PAGE 26 (score 33, numeric-paren 21, table-words 4, var-words 0) =====751752... continued from previous page:753Variable754OLS Model 1755OLS Model 2756OLS Model 3757IV Health758IV Health/Pain759GNS760SAC761Measures of model fit to data762Residual σ7630.22477640.21757650.2177660.21877670.2186768R27690.70357700.72237710.72357720.71917730.71957740.75747750.7568776¯R27770.70247780.72127790.72247800.71797810.7183782AIC783-2150.58784-3109.98785-3172.68786Tests of performance of variables in model787F788662.7***789641.4***790624.5***791Wald test792615***793615.9***794Weak instruments7951744.71***7961057.375***797Wu-Hausman79828.05***79930.969***800Sargan8010.332802Tests for spatial dependence and autocorrelation803LM spatial error8044877***8053561.8***8063511.8***807LM spatial lag808177.84***809259.98***810266.8***811Moran’s I81255.294***81354.548***814Partial first stage results for well-being instruments815Ln(Health+1)8160.1723***8170.1723***8180.1723***819σ8200.0068210.0068220.006823Ln(Pain+1)8240.0807***8250.0807***826σ8270.0058280.005829*** - p < 0.001, ** - p<0.01, * - p<0.05, † - p<0.1083025831832833===== PAGE 27 (score 26, numeric-paren 0, table-words 1, var-words 24) =====834835OLS Model 1 presents estimates that include only structure characteristics. The estimated parameters are largely as836would be expected. A one per cent change in floorspace is associated with a 0.65 to 0.66 percent increase in the transaction837price. A one percent change in lot size increases the transaction price by 0.05 per cent. The number of rooms, and availability838of bathrooms, balconies, and parking are all associated with an increase in the transaction price.839The presence of an attic, basement, and under-roof storage area are associated with lower transaction prices. The data840for usable interior surface area as described in NVM and VBO and VastgoedPRO and Vereniging Nederlandse Gemeenten841and Waarderingskamer (2018) specify that floorspace (floorm2) includes all usable area, which includes basements, attics,842and under-roof storage. The negative coefficients estimates arise from a portion of the interior area not being part of843the living area. Listed buildings (monuments) are more valuable than those that are not listed, and for non-monuments, a844monumental appearance leads to a smaller increase in price. Higher levels of interior and exterior maintenance are associated845with higher transaction prices, and similarly higher levels of insulation are associated with higher transaction prices, although846buildings with only one layer of insulation appear to sell for less than those with no insulation (reference group). Structures847from the 1980’s attract the lowest transaction prices.848OLS Model 2 adds the neighbourhood characteristics. Increases in density are associated with increases in transaction849price, which is consistent with the standard urban land use model presented in Alonso (1964). Controlling for that, larger850distances to an urban center (as a proxy for labor market access) are associated with lower transaction prices, burglaries are851associated with lower transaction prices, as is ethnic diversity.852OLS Model 3 includes subjective well-being and positive affect. Both coefficients are significant and positively related853to transaction price. This supports our hypothesis that higher transaction prices are associated with higher derived utility854from living in a house and location, which can be measured using subjective well-being. In addition, this model shows that855positive affect is associated with a higher transaction price, which supports the optimistic buyer hypothesis. The AIC’s856for these three models suggest that the fully specified model is the preferred one, decreasing from -2,151 for the property857characteristics model, to -3,110 for the model including neighbourhood characteristics, and finally -3,173 for the model858including subjective well-being.859The inclusion of subjective well-being in model 3 is associated with a number of small changes to the parameter estimates860for the usual variables included in a hedonic price model (model 2). The parameter estimate for floorspace, for example,861decreases by 0.0023, equivalent to 0.26 of the standard error of the parameter estimate. This suggests that the parameter862estimate in the standard hedonic is slightly biased upwards, meaning that some part of the increased willingness to pay863attributed to a larger floor area is actually due to the omitted attributes proxied through subjective well-being. We note a864similar change in the coefficients for the distance to forests and the distance to nature: the negative association in both865coefficients decreases when subjective well-being is included in the model. This indicates that part of the willingness to866pay attributed to proximity to nature is potentially due to some other unobserved characteristic associated with occupant867subjective well-being.868Similar to Anselin and Lozano-Gracia (2008) we test for spatial dependence in our data, and find robust Lagrange869Multipliers in excess of 3,511 for spatial dependence in the error term, and greater than 177 for spatial dependence in the87026871