Hedonisk prismodell Föreläsning Lund, 21 februari 2013 Mats Wilhelmsson Centrum för bank och finans (KTH) Institutet för bostadsforskning (Uppsala universitet) 1 21 februari 2013 Förmiddag Föreläsning två artiklar Eftermiddag Presentation av fem artiklar Två grupper 2 (1) Varför använda sig av hedonisk metodik? Värdera kostnader och nyttigheter Alla varor och nyttigheter har inte ett (explicit) pris. Vi måste skatta ett (implicit) pris Två metoder ”revealed preference” metod • fastighetsmarknaden ”stated preference” metod • frågeformulär 3 (2) Varför använda sig av hedonisk metodik? Som värderingsmodell Vi önskar ta fram ett förväntat pris på en fastighet mha historisk prisinformation. 4 (3) Varför använda sig av hedonisk metodik? Skatta prisutvecklingen på bostadsmarknaden mha historisk prisinformation www.valueguard.se 5 6 Gemensamt Regressionsanalys Kontrollera för att fastigheter ser olika ut ligger på olika platser har sålts vid olika tidpunkter 7 Prisvariation i rummet 8 Varför varierar fastighetspriser i rummet? Dvs. hur kan vi förklara priset om vi använder oss av tvärsnittsdata? Egenskaper Fastighetsknutna • Storlek, kvalitet, ålder Områdesknutna (läget, läget, läget) • Positiva och negativa externa effekter • Förekomst av kollektiva varor • Segmenterad marknad Relationen mellan fastighetens pris och fastighetens egenskaper skattas mha den sk hedoniska regressionen. Den hedoniska regressionen Den hedoniska regressionsmodellen är baserad på den hedoniska värdemodellen där vi antar att fastighetens pris är en funktion av fastighetens egenskaper. Den hedoniska regressionen kontrollerar för skillnader i egenskaper mellan fastigheter genom att sätta ett värde på dessa skillnader. Dvs. vi skattar implicita (hedoniska) priser på egenskaperna. 9 Haas (1922), Court (1937) och Rosen (1974) Den hedoniska regressionsmodellen Price P(Z ) 1F 2O 3T Egenskaper(Z) Fastighetsknutna(F) Områdesknutna (O) Tidsbundna (T) 10 Den hedoniska teorin max u(y,z i ) s.t. I Py y P( zi ) FOC u zi MU zi P p zi i zi u y MU y Skattade parametrar (koefficienter) i den hedoniska ekvationen är lika med den marginella betalningsviljan , dvs. de hedoniska priserna är lika med hur mycket vi är villiga att offra av andra varor för 11 att få egenskaperna. Den hedoniska teorin Första steget: estimera P(Z) Andra steget: estimera Zi g ( Pz , I ,...) i Price Det andra steget ger oss Egenpriselasticitet Inkomstelasticitet z 12 När är den använd? Värdering/fastighetstaxering Ersättning Skatta betalningsviljan på egenskaper Områdesegenskaper • Golfbanor, högspänningsledningar, sjöutsikt, detaljplaner, närhet till vägar (buller) etc Fastighetsegenskaper • Storlek, kvalitet, antal rum 13 Tiden • Indexkonstruktion Artikel 1: “The Impact of Traffic Noise on the Values of Single-family Houses” 14 Buller är något man är tar hänsyn till i planering av bostäder och infrastruktur. Buller är ett av de miljöproblem som är högst rankat i samhället. Trafik är den vanligaste källan till buller. Nästan 1,6 miljoner människor i Sverige är påverkade av buller i sin hem. Knappt 20% av dessa bor i bostäder där bullernivån är mer än 65 dBA. “The objective of the paper is to provide an empirical analysis of the impact traffic noise have on the values of single-family houses.” Buller och störning 15 0 decibel gränsen för vad ett friskt öra kan uppfatta 10 decibel mänsklig andning på 3 meters avstånd 20 decibel viskningar 50 decibel kraftigt regn 60 decibel normalt samtal 70 decibel vältrafikerad gata på 5 meters avstånd 75 decibel tvättmaskin 80 decibel dammsugare på 1 meters avstånd 85 decibel stadstrafik 90 decibel hårtork, gräsklippare 100 decibel traktor, slagborr på 2 meters avstånd 110 decibel konsert, motorsåg på 1 meters avstånd 120 decibel ambulans, knall från åska 130 decibel gränsen för smärta 140 decibel fyrverkeri, gevärsskott på 1 meters avstånd 150 decibel jetmotor på 30 meters avstånd, kan orsaka fysisk skada 200 decibel människor kan dö Design av studien 16 Under antagandet att negativa externa effekter kapitaliseras i fastighetspriserna så har jag använt en hedonisk modell. Mikrostudie (area 300 x 300 meter) Förort till Stockholm (Bromma) 1986-1995 Närhet till en större väg (Bergslagsvägen) Ungefär 300 fastigheter har sålts under perioden av totalt 1000 (vissa har sålts flera gånger) (30% omsättning på 10 år) Resultat: Närhet till genomfartsvägen kapitaliseras i fastighetspriserna upp till 30 %. 300 meter 1000 meter 17 NA SA Oberoende variabler 18 Bullervariabeln valuation positive effects net valuation 100 0 meter 1 300 other negative effects traffic noise -200 19 Ekonometrisk analys Modelspecifikation Price = b0 + b1. living area + b2. lot size + b3. age + b4. quality + b5. corner + b6. park + b7. FPI + b8. HV + b9. BBV + b10. VV + b11. SA + b12. SAliving area + b13. SAquality + b14. SAnoise + b15. SAlot size + b16. noise + b17. Noiseexp + ei 20 Log-linjär specification Resultat 21 Resultat SEK 1200000 1000000 800000 600000 400000 200000 0 dBA 50 52 22 54 56 58 60 62 64 66 68 70 72 74 76 78 80 Sammanfattning 23 Bullereffekten är stor. Statistiskt och ekonomiskt signifikant Den empiriska analysen tyder på att i genomsnitt så minskar värdet på en fastighet med 0,6% per decibel eller med hela 30% om vi jämför en fastighet som är utsatt för buller med en som inte är det. Den samhällsekonomisk nyttan att slippa buller värdera till 8000 kronor per person och år vid ljudnivåer över 72 dBA. Artikel 2: The Impact of Crime on Apartment Prices: Evidence of Stockholm, Sweden Vania Ceccato and Mats Wilhelmsson Royal Institute of Technology Stockholm, Sweden 24 Crime rates, Stockholm 25000 4500 Total crime 4000 Crime per 100.000 inhabitants 20000 3500 Vandalism 3000 15000 2500 Thefts 2000 10000 1500 Violent crime 1000 Robbery Residential burglary 500 5000 0 0 1997 25 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Residential burglary, 2008 26 Introduction 27 Researchers have long suggested that high crime levels make communities decline. This decline may translate into an increasing desire to move, weaker attachments of residents and lower house values. This is because buyers are willing to pay more for living in neighbourhoods with lower crime rates or, alternatively, buyers expects discounts for purchasing properties in neighbourhoods with higher crime rates. Introduction International literature, that is heavily based on North American and British evidence, shows somewhat inconclusive findings. Little empirical evidence exists under the Swedish conditions. This study aims at assessing the impact of crime on apartment prices using Stockholm City as study area. 28 Contributions This analysis explores a set of land use attributes created by spatial techniques in hedonic pricing modelling. If a low crime area is surrounded by high crime, then criminogenic conditions at that area may be underestimated because of the high levels of crime in neighbouring zones. GIS and spatial statistics techniques are used to tackle this problem, so the neighbourhood structure is added to the model to capture crime conditions at each unit of analysis but also in its neighbouring units. 29 Theory and empirical findings The effect of crime on housing prices is well documented. Evidence from the last three decades confirmed that crime had a significant impact on house prices (Hellman and Naroff, 1979, Rizzo, 1979, Dubin and Goodman, 1982, Clark and Cosgrove, 1990, Feinberg and Nickerson, 2002, Titta et al., 2006, Munroe, 2007). In the UK, the effect of crime on property prices does not seem the same across crime types. In Gibbons (2004) residential burglary had no measurable impact on prices, but criminal damage did affect negatively housing prices. 30 Since the seminal work by Thaler (1978) showing that property crime reduces house values by approximately three percent, studies have shown evidence of similar effect. One explanation for this is that vandalism, graffiti and other forms of criminal damage motivate fear of crime in the community and may be taken as signals or symptoms of community instability and neighbourhood deterioration in general, pulling housing prices down. Our hypotheses 1. 2. 3. 4. Crime impacts negatively on apartment prices after controlling for attributes of the property and neighbourhood characteristics. Different types of crimes affect property values differently. The price of an apartment is dependent on the crime levels at its location as well as the crime levels in the surrounding areas. Parameter heterogeneity in crime effects on apartment prices. 31 Estimation approach 32 OLS (benchmark model) Spatial dependency (spatial lag and error model) A binary weight matrix based on shared common boundaries was created to represent the spatial arrangement of the city. Endogeneity (instrument variable approach) The causal relationship between apartment prices and crime seems to go in both directions. Areas with high apartment prices may attract burglars and therefore will the number of burglaries be high in high priced neighborhoods. Different aspects of crime Parameter heterogeneity (in space) Spatial dependency Pris=100 Fel=-50 Pris=200 Fel=50 ”Bästa gissning”=150 Spatial dependency Omitted variables $100 $200 ”Best guess”=100 and 200 Instrument variable approach (according to Wikipedia) IV methods allow consistent estimation when the explanatory variables are correlated with the error terms. In attempting to estimate the causal effect of some variable x on another y, an instrument is a third variable z which affects y only through z's effect on x. y a bx e 1 x c0 c1 z u 2 y a bxˆ e 5 Models y 1 x 2C 3WC y 1 xˆ 2C 3WC 1 2 xˆ z y 1 xˆ 2C 3WC Wy y 1 xˆ 2C 3WC 3 4 5 36 W y 1 xˆ 2C 3WC 4CI CN W The data Apartment prices (arm-length) 9622 transaction, 2008 The capital of Sweden - Stockholm Property attributes Location attribute Total crime rate, robbery, vandalism, violence, burglary, shoplifting, drugs, theft, theft of cars, theft from cars, assault. Instrument variable 37 Distance to CBD, distance to water, subway station, commuting train station, highway, main street. Crime data Living area, no. of rooms, monthly fee, age, elevator, balcony, floor Homicide Location attributes Sold apartments in relation to water bodies, buffer of 100 meters (red), 300 meters (orange) and 300 meters (yellow). 38 Location attributes Sold apartments in relation to subway stations, buffer of 100 meters (red), 300 meters (orange) and 300 meters (yellow). 39 Descriptive statistics Average Crime Robbery Vandalism Violence Burglary Shoplifting Drugs Theft Theft of cars Theft from cars Assault 40 Crime rate per 10,000 inhabitants Robbery per 10,000 inhabitants Vandalism per square meter of area Outdoor violence per 10,000 inhabitants Residential burglary per 10,000 inhabitants Shoplifting per 10,000 inhabitants Drug related crimes per 10,000 inhabitants Theft per square meter of area Theft of cars per square meter of area Theft from cars per square meter of area Assaults per 10,000 inhabitants 7963.494 93.776 4.306 281.5881 Standard deviation 254881.1 3141.251 5.158 8905.225 51.481 92.930 1880.947 434.534 95930.84 13668.88 8.715 .406 1.053 11.222 .304 .786 188.569 5988.247 The hedonic equation – property attributes OLS OLS & Instrumental Lag & Instrumental Error & Instrumental Coefficient t-values Coefficient t-values Coefficient z-values Coefficient z-values .7043 53.37 .7054 53.19 .5919 53.64 .6360 65.91 .1889 15.85 .1878 15.68 .2013 20.66 .1898 22.73 -.1195 -18.97 -.1194 -.18.95 -.0723 -14.05 -.0513 -11.07 .1938 13.58 .1931 13.52 .1213 10.42 .0603 4.98 .1142 13.35 .1119 12.47 .1169 15.98 .0240 2.85 -.0277 -3.00 -.0277 -3.00 .0282 3.74 -.0073 -.85 -.2044 .01 -.2047 -18.12 -.1030 -11.11 -.0631 -5.80 -.1729 .01 -.1739 -13.74 -.1149 -11.10 -.1194 -8.78 .1117 4.77 .1157 4.83 .0674 3.46 .1134 5.50 -.0389 -4.69 -.0392 -4.68 -.0493 -7.22 -.0301 -4.51 .0164 9.10 .0163 9.07 .0158 10.81 .0157 12.31 -.0103 -.98 -.0101 -.957 -.0028 -.33 .0038 .53 -.0084 -1.04 -.0086 -1.07 -.0139 -2.16 -.0049 -.87 -.0345 -4.65 -.0345 -4.64 -.0292 -4.83 -.0216 -4.36 .0245 3.56 .0243 3.54 .0224 4.01 .0289 6.21 Area Room Fee Age1 Age2 Age3 Age4 Age5 New Elev Elev*floor Balc Elev*Balc First Top Small differences between OLS, IV and the spatial models. 41 The hedonic equation – Location attributes OLS OLS & Instrumental Lag & Instrumental Error & Instrumental Coefficient t-values Coefficient t-values Coefficient z-values Coefficient z-values .1054 9.33 .1051 9.30 .0398 4.32 .0335 2.31 .0218 2.48 .0202 2.63 .0210 2.88 .0117 .92 .1005 12.84 .1010 12.87 .0609 9.49 .0721 5.95 .0186 1.89 .0210 2.04 .0047 .57 .0207 1.66 .0451 6.25 .0461 6.29 .0294 4.93 .0265 2.70 .0305 3.85 .0318 3.94 .0323 4.92 -.0070 -.56 -.0236 -.71 -.0230 -.69 -.0351 -1.31 -.0223 -.97 -.0725 -3.64 -.0734 -3.68 -.0737 -4.55 -.0334 -.16 .0292 2.23 .0315 2.35 .0338 3.10 -.0029 -.13 -.0986 -2.98 -.0976 -2.95 -.0637 -2.37 -.0325 -.97 .0116 0.80 .0122 .84 .0272 2.30 -.0032 -.16 .0326 3.28 .0304 2.94 .0214 2.55 -.0022 -.13 .0159 2.25 .0164 2.31 -.0001 -.1708 .0010 .12 .0493 5.81 .0481 5.60 .0362 5.18 .0479 4.22 -.0835 -8.84 -.0818 -8.46 -.0481 -6.10 .0058 .41 -.3599 -70.43 -.3600 -70.43 -.1926 -38.82 -.2630 -23.82 Water100 Water300 Water500 Sub100 Sub300 Sub500 Train100 Train300 Train500 Road100 Road300 Road500 Main100 Main300 Main500 Distance 42 Effect of seaview Effect of subway 30% 12% Water100 Water300 Sub100 Water500 25% 10% 20% 8% 15% 6% 10% 4% 5% 2% Sub300 Sub500 0% 0% OLS IV SAR OLS SEM IV SAR SEM Effect of main street Effect of highway 6% 6% 4% 4% Main100 Main300 Main500 2% 2% 0% 0% OLS OLS IV SAR SEM -2% -2% -4% -4% -6% -8% -6% 43 -8% Road100 Road300 Road500 -10% IV SAR SEM The hedonic equation – Crime variables Total crime W_tot crime W_Y Lambda R-square Adj R-square AIC Moran’s I OLS OLS & Instrumental Lag & Instrumental Error & Instrumental Coefficient t-values Coefficient t-values Coefficient z-values Coefficient z-values .0011 .20 -.0089 -.79 .0068 .74 -.0418 -2.79 -.0479 -6.61 -.0455 -8.53 -.0314 -7.24 -.0229 -2.71 .4936 64.68 .8024 104.31 .7674 .7675 .8453 .8850 .7662 .7662 863 863 -2348 -4026 .50 0.50 - If total crime increase by 1 percent, apartment prices are expected to fall by 0.04 percent. 44 If total crime in the surrounding areas increase by 1 percent, prices are expected to fall by 0.02 percent. Different measures of crime Robbery Coefficient OLS Error -.0049 -.0037 (-.45) (-2.43) -.0470 -.0028 (-15.7) (-4.64) - Robbery W_Robbery Vandalism Vandalism Coefficient OLS Error - -.0340 (-2.27) -.0.184 (-4.83) Burglary Coefficient OLS Error - Assault Coefficient OLS Error - - - - - - - - - - - W_Vandalism - Burglary - -.0058 (-2.80) .0035 (4.32) - W_Burglary - - Assault - - W_Assault - - -.1468 (-2.14) -.0514 (-13.27) -.2110 (-2.16) -.0044 (-5.16) .0013 (.0923) -.0358 (-13.05) -.0503 (-2.45) -.0213 (-3.80) Theft W_Theft R-square AIC Moran’s I on residuals 45 .7720 691 0.49 .8848 -4036 .7662 914 0.50 Theft Coefficient OLS Error - .8854 -4035 .7702 762 0.50 .8849 -4041 .7700 768 0.49 .8457 -4030 If burglary increases by 1 percent, apartment prices are expected to fall by 0.21 percent. -.0792 (-6.20) .0442 (9.74) .7681 843 0.50 -.0563 (-3.26) .0832 (8.92) .8854 -4094 Parameter heterogeneity Burglary W_Burglary Burglary *Inner Burglary *North Adj R-square R-square AIC Moran’s I 46 Inside inner circle Coefficient OLS Error -.3730 -.2487 (-5.43) (-2.59) -.0358 -.0345 (-9.31) (-4.27) .2500 .1093 (9.92) (3.79) . 7893 .7880 1.35 0.47 .8857 -4246 North Coefficient OLS -.2002 (-3.00) -.0372 (-9.61) .0300 (1.41) .7870 .7857 97.8 0.47 Error -.1836 (-1.92) -.0353 (-4.33) -.0270 (-.85) .8857 -4233 Inside inner circle and north Coefficient OLS Error -.3716 -.2353 (-5.38) (-2.45) -.0357 -.0345 (-9.26) (-4.26) .2508 .1159 (9.81) (3.97) -.0040 -.0392 (-.19) (-1.45) . 7893 .7880 .8857 3.32 -4246 0.48 Conclusion Findings indicate that if total crime increase by 1 percent, apartment prices are expected to fall by 0.04 percent. Different from what was initially hypothesized, residential burglary (and not vandalism) seems to have the largest effect on property values. If residential burglary increases by 1 percent, apartment prices are expected to fall by 0.21 percent. It seems that the expected ‘visual effect’ vandalism has on people’s perception of an area is not strong enough to affect property prices in the case of Stockholm. Results show that magnitude of the effect of residential burglary on apartment prices is highest in the northern part of Stockholm than in the South. Apartments in inner city areas are also less discounted than the ones located of the central areas. One of the most important results of this research is the indication that price of an apartment is dependent on the crime levels at its location as well as the crime levels in the surrounding areas, regardless crime type. 47