Tjalling C. Koopmans Research Institute

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1 Tjalling C. Koopmans Reseach Institute

2 Tjalling C. Koopmans Reseach Institute Utecht School of Economics Utecht Univesity Kiekenpitplein EC Utecht The Nethelands telephone fax website The Tjalling C. Koopmans Institute is the eseach institute and eseach school of Utecht School of Economics. It was founded in 003, and named afte Pofesso Tjalling C. Koopmans, Dutch-bon Nobel Pize laueate in economics of In the discussion papes seies the Koopmans Institute publishes esults of ongoing eseach fo ealy dissemination of eseach esults, and to enhance discussion with colleagues. Please send any comments and suggestions on the Koopmans institute, o this seies to çåíïéêé=îççêää~çw=tofh=íêéåüí How to each the authos Please diect all coespondence to the fist autho. Matijn I. Döes ~^ ~Utecht Univesity Utecht School of Economics Kiekenpitplein TC Utecht The Nethelands. Hay Gaetsen Univesity of Goningen Faculty of Economics and Business PO Box AV, Goningen The Nethelands Phone: (+31) Walte J.J. Manshanden ^TNO, Built Envionment and Geosciences Van Mouik Boekmanweg 6 68 XE Delft The Nethelands Phone: (+31) This pape can be downloaded at:

3 Utecht School of Economics Tjalling C. Koopmans Reseach Institute Discussion Pape Seies 1-03 The Divesification Benefits of Fee Tade in House Value Matijn I. Döes ac Hay Gaetsen b Walte J.J. Manshanden c a Utecht School of Economics Utecht Univesity b Univesity of Goningen Faculty of Economics and Business c TNO Built Envionment and Geosciences Febuay 01 Abstact This pape finds that homeownes could substantially educe house pice isk if they would einvest thei housing wealth in a maket potfolio of houses. Fee tade in the value of the house among homeownes would allow them to do so. To quantify the divesification benefits of fee tade in house value, we estimate simple CAPM and APT models based on a detailed panel dataset of house pice changes in the Nethelands. We find that about 9 to 96 pecent of house pice isk is divesifiable. In most cases, these divesification benefits outweigh the hedging effectiveness of house pice futues. Keywods: house pice isk, fee tade, financial maket, divesification, futues JEL classification: G10; G11; G15; R30 Acknowledgements We would like to thank Statistics Nethelands and the Kadaste fo poviding access to the dataset. In addition, we benefited fom comments given by Rob Alessie, Wolte Hassink, and semina paticipants of the EEA 011 confeence.

4 1. Intoduction The ecent decline in house pices in many counties has seved as a eminde that house pice isk may be substantial and that the consequences of this isk may be sevee. House pice isk is elatively high fo homeownes in compaison to standad institutional investos since the typical homeowne cannot adequately divesify the housing investment acoss locations o maket segments. As a esult, Case et al. (1991) and Shille (008) have advocated the establishment of deivatives makets fo home pices. They ague that homeownes could sell futues based on house pice indices to hedge thei exposue to house pice isk. Although the establishment of deivative makets deals with the poblem of the sizeable tansaction costs associated with investing in a divesified housing potfolio, it still ignoes anothe main eason as to why house pice isk is elatively high fo homeownes. This isk is high because most homeownes only invest in a single house. In paticula, the indivisibility of the housing investment impais the homeownes investment allocation, especially since the typical homeowne has only limited wealth to invest in housing. As a esult, a homeowne usually does not hold a divesified housing potfolio. The aim of this pape is to investigate the eduction in house pice isk (divesification benefits) if a homeowne could einvest his housing wealth in a maket potfolio of houses. We ague that fee tade in house value would allow homeownes to invest in each othe s popety and, consequently, to divesify (shae) house pice isk. Homeownes could feely tade in the value of the house if the housing investment would be divisible and tansaction costs would be low. A housing stock maket could, fo example, facilitate such tade. 1 Since fee tade in house value cuently does not exist, the analysis in this pape is a countefactual analysis. To quantify the divesification benefits, we estimate Capital Asset Picing Method (CAPM) and Abitage Picing Theoy (APT) models. Although it is common in the finance liteatue to investigate divesification benefits and hedging effectiveness using these models, these methods have not been widely applied in a housing maket setting. To estimate these models, we use a dataset of quately house pice changes pe municipality and type of house in the Nethelands between 1995 and 008. The main advantages of this dataset ae twofold. Fist, this dataset contains house pice data pe type of house. As a esult, we can also 1 This pape does not discuss in detail how such a stock maket could be established. Rathe, ou esults ae simply meant to povide empiical evidence about the usefulness of a financial maket to deal with house pice isk. Fo a shot discussion, see the conclusion and discussion section. In a notable exception, Case et al. (009) estimate housing CAPM models and APT models based on quately house pice etuns at the MSA level. They show that thee is a stong positive isk-etun elationship in the US housing maket. 1

5 investigate divesification acoss maket segments. Second, the Nethelands may be compaable to lage MSAs such as the New Yok MSA. 3 Hence, the analysis in this pape may be intepeted as a highly detailed within-msa analysis. By contast, most pevious studies have focused on coss-msa house pice vaiation (e.g. Sinai and Souleles, 009; Case et al., 009). Howeve, since most households move within MSAs, it is especially the vaiation in house pice changes within MSAs that contibutes to the isk of owning a home. To compae ou esults with the isk-educing benefits of a deivatives maket, we also discuss the effectiveness of hedging with house pice futues. The divesification benefits of fee tade in house value ae expected to be high based on a county-wide potfolio of houses. Instead, hedging with futues may be moe effective if those futues ae based on highly disaggegate (egional) house pice indices. Hence, ou eseach also povides novel evidence about the isk-educing benefits of divesification vesus the hedging effectiveness of house pice futues. The emainde of this pape is oganized as follows. Section discusses the pevious liteatue. Section 3 pesents the data and methodology. Section 4 epots the egession esults. Section 5 concludes.. Pevious liteatue In a emakable feat of foesightedness, Case et al. (1991) aleady advocated the establishment of deivative makets fo home pices duing the 1990s: We need instead some othe medium, that allows eal estate ownes to hedge the isk of thei eal estate while at the same time owning the eal estate. What is needed is some maket that stands between individual popety ownes and boade potfolio investos, allowing the potfolio investos to shae the isk of the popety without owning it. What is needed, inheently, ae futue and option makets cash settled on indexes of eal estate pices. (Case et al, 1991, p. 6) In ecent yeas, this idea has gained enewed momentum as a esult of the impact of the subpime cisis (i.e. see Shille, 008). To some extent, homeownes could have educed thei house pice isk by option o futues contacts based on house pice indices. 4 5 In paticula, homeownes could sell house pice futues to institutional investos as a hedge against house pice isk. Betus et al. (008) show that such a stategy (tade of futues on the Chicago 3 Fo instance, the population in the Nethelands is about 16.5 million in 009, which is compaable to the numbe of people in the New Yok MSA of about 19 million in 009. In addition, the Nethelands consists of an uban coe and a pheiphey. A typical MSA has a simila stuctue. 4 Fo a discussion of futues based on the OFHEO index in the US, see Deng and Quigley (008). 5 Hinkelman and Swidle (008) show that existing CME futues contacts do not povide an effective hedge. As a esult, they ague that futues based on house pice indices may povide homeownes with a valuable hedging oppotunity. Altenatively, Englund et al. (00) find that homeownes can also hedge thei lumpy investment in housing (i.e. in Stockholm) with stock and bonds.

6 Mecentile Exchange (CME)) could have educed the homeowne s house pice isk by about 88 pecent in Las Vegas ove the peiod In addition, Quigley (006) finds that futues makets based on house pice indexes could have inceased potfolio etuns fo Euopean investos by seveal pecentage points at the same level of isk. It is fai to say that eal estate deivative makets ae still not widely used by homeownes. In many counties these makets do not exist (anymoe) o they ae still of mino impotance. 6 De Jong et al. (008) povide a possible eason why house pice deivatives makets have failed to take off. They ague that futues based on the Case-Shille city pice index in the US may not be effective hedging instuments since the expected etuns on these futues is positive (and homeownes would in geneal shot sell futues). Moeove, they find that the idiosyncatic isk within a city is too lage to use futues as an effective hedging stategy. An altenative explanation fo the absence of a fully functioning deivatives maket based on house pices may be the hedging benefits of homeowneship (i.e. see Sinai and Souleles, 009). In paticula, the cuent home may be used as a hedge against futue housing costs. Specifically, a change in the pice of the cuent home may cancel out the change in the pice of the futue home. Since selling futues may lead to a simila negative exposue as buying a new home, the use of both hedging instuments may easily unhedge the homeowne (Sinai and Souleles, 009). Thee ae two notable diffeences between hedging with futues and hedging with the cuent house. Fist, a futues contact needs to be financed by own cash/savings, while a new home may be patly financed by the old home (and a motgage). Since the housing investment is usually too lage to be fully paid by the homeowne himself, the typical homeowne may not have enough additional pivate wealth to fully hedge his exposue to house pice isk with futues. A second diffeence is that the choice of investing in a house is also based on housing consumption. In paticula, thee may be a consumption demand and an investment demand fo housing (i.e. see Ioannides and Rosenthal, 1994). Pat of the investment demand may even be hedging demand (i.e. see Cocco, 000; Han, 008; Sinai and Souleles, 009). As a esult, the natual hedge against house pice isk is likely to be impefect since the investment decision may well be distoted by the housing consumption choice. 6 In 001, the fim City Index intoduced spead betting based on house pices in London, while IG Index launched its own spead betting in 00. Both makets wee closed by 004. In May 006, the CME intoduced house pice options and futues. Howeve, until Novembe 007 the notional value of these contacts only eached about 61 million dollas (fo a discussion see Shille, 008). In Mach 009, the Fankfut-based Euex stated its tade in house pice futues (fo commecial eal estate). Until August 009, the value of the taded futue contacts was only 15 million euos (see Piazolo, 010). 3

7 Finally, Caplin et al. (003) have agued fo insuance against deceases in house pices. The poblem with this appoach is that the investment and the isk associated with this investment may be so substantial that it is questionable whethe homeownes can affod the insuance pemium to insue against house pice isk. In addition, if thee is a maket bust, the financial buden on insuance companies may simply become too high to bea. Consequently, it may be too isky to povide such insuance to homeownes in the fist place. The studies mentioned above discuss some of the possibilities to educe house pice isk. In paticula, a homeowne has invested in his cuent home and he may insue, hedge, o divesify (with othe assets), his exposue to house pice isk. Nevetheless, all of these studies ae based on the fact that the housing investment is fixed. That is, the housing investment itself is not divesified. In paticula, two standad featues of housing maket models ae that selling o buying a home is associated with substantial tansaction costs and that the housing investment is indivisible (e.g. see Flavin and Nakagawa, 008; Han, 008). The second housing maket featue suggests that homeownes need to invest a lage sum of money to obtain a house at a paticula location. Given the limited wealth of a household, a household may not feely divesify the housing investment acoss locations. Tansaction costs add to the distotion in the investment allocation. Both of these housing maket featues make the housing investment illiquid (i.e. a lumpy investment). As mentioned, this pape investigates the divesification benefits of fee tade in house value. As such, we do not necessaily ague fo tade between individual homeownes and boade potfolio investos (i.e. Case et al., 1991), but we also emphasize the benefits of tade between individual homeownes. 3. Data and methodology This pape uses quately house pice changes in the Nethelands between 1995 and 008. These pice changes ae based on the median house pice pe municipality i, type of house, and time t. We used all administative tansaction pices of existing homes between 1995 and 008 to calculate the median pices. 7 The median pices ae based on at least 4 tansaction pices. Thee ae 5 types of houses available in the dataset: apatments, ow/teaced houses, cone houses, semi-detached houses, detached houses. In what follows, we use fo these types of houses the abbeviations AP, RH, CH, SH, DH, espectively. Thee ae 441 municipalities in the Nethelands. Theefoe, if thee would be no missing obsevations, thee 7 By law, these pices wee ecoded by the Kadaste. The Kadaste povided the dataset to Statistics Nethelands. Statistics Nethelands ganted us access to this dataset ( Bestaande Koopwoningen 0081V1 ). 4

8 should be 11,75 pice change obsevations (55 quates * 5 types of houses * 441 municipalities). Howeve, due to missing values thee ae only 84,038 pice change obsevations available in the dataset. The quately aveage numbe of municipalities with a non-missing house pice change is 188 fo apatments (RH: 376; CH: 303; SH: 36; DH: 334). The aveage time dimension of the etun seies is about 3 quates fo apatments (RH: 48; CH: 40; SH: 43; DH: 43). Table 1: Desciptive statistics, quately house pice changes and contols, Mean Std. dev. p5 p50 p75 N. Obs. House pice changes Apatments ,37 log p i, t, = 1 (x100%) Row houses ,704 log p i, t, = (x100%) Cone houses ,69 log p i, t, = 3(x100%) Semi-detached houses ,9 log p i, t, = 4 (x100%) Detached houses ,393 log p i, t, = 5 (x100%) All house types ,038 log pi, t, (x100%) Contols log GDPt (x100%) ,038 I t (Euibo, pecentage) ,038 Souce: Houses pices ae fom the Kadaste, the GDP data is fom Statistics Nethelands (GDP at maket pices, cuent pices), the 3-month Euibo inteest ate is taken fom the OECD. Notes: This table is based on house pice changes fo 441 municipalities. The Euibo inteest ate is a quate-specific ate that is annualized. Table 1 epots the desciptive statistics of the pice changes pe type of house and two contol vaiables: GDP gowth and the 3-month Euibo. Table 1 suggests that the aveage quately pecentage etun on houses has been about 1.8 pecent. This etun seems to be elatively high fo detached houses (.0 pecent) and apatments (1.9 pecent). In addition, the spead of etuns fo these two types of houses is also elatively high. Moeove, Table 1 shows that the quately nominal GDP gowth is 1.3 pecent and the quately 3-month Euibo inteest ate (annualized) is 3.8 pecent. In this pape, we will also use house pice changes fo 40 standad egions (40 COROPs, NUTS-3 classification) in the Nethelands. The aconym COROP is named afte the commission that defined these egions in These egions ae in accodance with egional labo/housing makets in the Nethelands. The aveage quately pice changes acoss 5

9 COROPs g, including the minimum and maximum egional etun, is epoted pe type of house in Table. Table : Desciptive statistics, quately house pice changes acoss egions, Min. Mean Max. House pice changes Apatments log p g, t, = 1(x100%) Row houses log p g, t, = (x100%) Cone houses log p g, t, = 3 (x100%) Semi-detached houses log p g, t, = 4 (x100%) Detached houses log p g, t, = 5 (x100%) Notes: This table is based on aveage house pice changes fo 40 COROP egions. Table suggests that thee is substantial heteogeneity in house pice changes acoss egions. In paticula, etuns fo apatments ae highest at Kop van Nood-Holland (5.6 pecent) and lowest at Nood-Denthe (-.1 pecent). With egad to ow houses, the quately pice changes ae highest at the egion Het Gooi en Vechtsteek (. pecent) and lowest at Delftzijl en omgeving (1.1 pecent). Cone houses seem to have a high etun in Oost- Goningen (. pecent) and a low etun in Zuid-West Fiesland (-0.1 pecent). Moeove, the egion Noodoost-Nood Babant has a elatively high etun with egad to semi-detached houses (.6 pecent), while Delft en Westland has a elatively low etun (-0.4 pecent). Finally, the pice changes of detached houses ae highest at Delftzijl en omgeving (3.3 pecent) and lowest at IJmond (-1.1 pecent). To investigate the divesification benefits of fee tade in the value of the house, we estimate the following CAPM type of models: log pi, t, = β0, i, + β1, i, log p t, + εi, t,, (1) whee log p i, t, is the diffeence in the logaithm (appoximate pecentage change) of the median tansaction pice at municipality i, time t, and of house type, the tem log p t, is the coss-sectional aveage pecentage pice change, and ε i, t, is the eo tem. To avoid 6

10 endogeneity, log p i, t, is excluded fom the coss-sectional aveage pecentage pice change fo each i (we make this coection thoughout this pape). We estimate equation (1) pe municipality and type of house (i.e. time seies egessions). The size of β 1, i, captues the sensitivity of the house pice changes pe municipality to the fluctuations in the total housing maket etuns. Specifically, ou estimates will suggest whethe the housing investment in a municipality is an aggessive ( β 1,, > 1) o defensive investment ( β 1,, < 1) elative to the maket potfolio. In addition, the total housing i investment divided by the beta coefficient equals the total amount that an investo (e.g. homeowne) would need to invest in house pice futues to hedge his exposue to house pice isk (i.e. to hedge against the vaiation in log p i, t, ). We measue the divesification benefits of fee tade in house value by the coefficient of detemination. In paticula, the vaiation in etuns that is associated with the vaiation in maket etuns, R i,, captues the undivesifiable (maket/county/systematic) isk. By i contast, 1 Ri, is ou measue of the divesifiable (idiosyncatic) isk. 8 This measue captues the eduction in the vaiation of house pice etuns if the homeowne could invest the value of his house in a maket potfolio of houses. Hence, if we find a low R the i, divesification benefits of fee tade in house value ae high. Instead, the hedging effectiveness of futues is exactly opposite to the divesification benefits of fee tade in house value. In paticula, a high R is associated with a high hedging effectiveness. As mentioned, i, we will compae the divesification benefits of fee tade in house value with the hedging effectiveness of futues to examine which one is moe effective in educing house pice isk. We also estimate seveal extensions of the basic CAPM model. In paticula, equation (1) investigates the divesification benefits of a homeowne who owns a house of type and would invest the value of this house in a maket potfolio of houses of type. Howeve, it may be inteesting to examine whethe this homeowne could obtain additional divesification benefits if he would divesify acoss maket segments. As a consequence, we also estimate an 8 This appoach does not deviate substantially fom the method used by Case et al. (009). In paticula, they use the standad deviation of the esiduals as a measue of divesifiable isk. Since the esiduals have an expected value of zeo by constuction, this measue is equal to the Sum of Squaed Residuals (SSR). The measue we use benchmaks the SSR to the total vaiation in etuns (SST). 7

11 extended CAPM model whee the etuns pe municipality i ae egessed on the aggegate etuns of all types of houses : 9 log p = θ + θ log p t + η, () i, t, 0, i, 1, i,, i, t, 1 whee the summation pat of equation () captues the maket etuns fo the 5 types of houses and η i, t, is the eo tem. Again, we will estimate this model pe municipality and type of house. A futhe issue is that equation (1) does not take into account additional systematic isk factos. As a esult, we also estimate APT type of models. In paticula, we include GDP gowth, log GDPt, and the Euo Intebank Offeed Rate (3-month Euibo), I t, as additional contol vaiables in equation (1): log p = λ + λ log p + λ log GDP + λ I + µ, (3) i, t, 0, i, 1, i, t,, i, t 3, i, t i, t, whee µ i, t, is again the eo tem. Although thee may be othe systematic isk factos, we only add the afoementioned two contol vaiables. We ague that these contol vaiables may captue additional impotant isk factos associated with owning a home (i.e. the isk of default, motgage inteest ate isk). Fo instance, especially duing an economic bust (i.e. log GDP is low) the isk of motgage default may be elatively high since an economic bust t is usually associated with a decease in house pices and a elatively high chance that homeownes may loose thei job. With egad to the second contol vaiable, the Euibo inteest ate, we ague that vaiation in this inteest ate may captue (motgage) inteest ate isk. In paticula, the motgage inteest ate is the Euibo inteest ate plus a isk pemium, which depends on the iskiness of the motgage. 10 Finally, equation (1) examines the divesification benefits if the owne of a home invests his housing wealth in a Dutch housing maket potfolio. Howeve, it is also inteesting to investigate the divesification benefits if a homeowne could simply invest in a egional 9 The etuns on the othe types of houses than the type of house unde consideation ae not intepeted as a systematic isk fo this type of house. Hence, we do not intepet this model as an APT model. Instead, we focus on a homeowne who invests in a maket potfolio consisting of all 5 types of houses. 10 The Euibo inteest ate may also be intepeted a poxy fo the iskless ate of etun. 8

12 potfolio of houses. In paticula, the divesification benefits based on a egional potfolio of houses may be less than the divesification benefits of investing in a total maket potfolio since a egional potfolio does not cove against the coss-egional vaiation in house pice changes. By contast, futues based on egional house pices may be moe effective since these etuns ae moe likely to be simila to the homeowne s etuns than the highly aggegated Dutch housing maket etuns. 11 As such, it is inteesting to examine whethe in this case futues would be moe effective than fee tade in educing house pice isk. As a consequence, we also estimate CAPM models based on egional aveage etuns: log pi, t, = δ0, i, + δ1, i, log p g, t, + ωi, t,, (4) whee log p g, t, is the aveage pice pe time t, type of house, and egion g (again this aveage excludes log p i, t, fo each i), and ω i, t, is the eo tem. As mentioned, we use 40 standad egions. 4. Regession esults Table 3 epots some desciptive statistics (aveage slope coefficient, aveage R-squaed acoss municipalities) of the time seies estimates of equations (1) to (4). The fist panel in Table 3 shows the basic CAPM model estimates pe type of house based on equation (1). These estimates suggest that on aveage a house is a defensive investment elative to the maket potfolio. In paticula, the aveage slope coefficients ae less than one acoss all types of houses. That is, the municipal-specific etuns do not seem to be vey sensitive to changes in the maket etun. Specifically, the aveage coefficient anges fom 0.50 fo apatments to 0.71 fo ow houses. This esult implies that a homeowne who would like to hedge his exposue to house pice isk/housing maket isk would need to sell 1.4 to.0 euos in futues contacts pe euo investment in the house. Although the aveage coefficients ae below one, thee is a substantial faction of the municipalities, between 45 to 55 pecent of the total numbe of municipalities, in which the house is a elatively aggessive investment. Based on the CAPM estimates it is also possible to quantify the extent to which (undivesifiable) house pice isk is piced. The diffeence in the annualized maket etuns 11 Pefeably, futues should be based on the individual homeowne s house pice etuns (tailo made). Howeve, given the heteogeneity in etuns, these contacts would no longe be standadized, which would impai the tade in those contacts. As a consequence, city/egional aveage housing etuns may be moe suitable to base futues constacts on. 9

13 and the 3-month Euibo (isk fee ate) times the aveage slope coefficient pe type of house suggests that the yealy isk pemium on housing is about 4.6 pecent fo apatments (i.e. ( ) * 0.5), 3. pecent fo ow houses, 3.0 pecent fo cone houses, 4. pecent fo semidetached houses, and 8.3 pecent fo detached houses. These esults indicate that especially apatments and detached houses ae elatively isky to invest in. A futhe esult with egad to the egession coefficients epoted in the fist panel of Table 3 is that only 6 to 10 pecent of the egession coefficients ae statistically significant at the 5 pecent significance level. This esult is also eflected in the elatively low aveage R- squaed coefficient pe type of house. This finding is a fist indication that the divesification benefits of fee tade in house value may be substantial. As mentioned, we estimate the divesification benefits of fee tade in the value of the house by one minus the R-squaed. The R-squaed estimates with egad to the basic CAPM models suggest that the owne of a type of house could educe the vaiation in house pice changes by 9 to 96 pecent if he would einvest his housing wealth in a maket potfolio of houses of type. 1 The emaining 4 to 8 pecent of the vaiation in house pice changes epesents the systematic isk a homeowne cannot divesify against by investing in the total housing maket potfolio. These esults imply that homeownes could substantially educe house pice isk by investing thei housing wealth in a maket potfolio of houses. Even though shaing the (pice) isk and etuns on housing wealth educes house pice isk, it cannot hedge the homeowne against maket wide shocks (e.g. financial cisis). Finally, the low aveage R-squaed estimates in the fist panel of Table 3 suggests that house pice futues based on the maket aveage house pice would have a elatively low hedging effectiveness. In paticula, the hedging effectiveness only dominates the divesification benefits in 0. to 4 pecent of the municipal-specific egessions (Rsquaed>0.5). This esult implies that in most cases the divesification benefits of fee tade in house value seem to outweigh the hedging effectiveness of house pice futues. The extended CAPM model estimates, see equation (), ae epoted in the second panel of Table 3. As mentioned, the extended CAPM model is used to estimate the divesification benefits if, fo instance, the owne of an apatment would einvest his housing wealth in a maket potfolio consisting of all types of houses. Table 3 indicates that the divesification benefits of such a stategy would be 79 to 85 pecent, depending on the type of 1 Case et al. (009) estimated simila CAPM models fo the US. The esults of thei basic housing CAPM model suggest that about 81 pecent of the MSA etun vaiation may be divesifiable. Thei egession esults cooboate ou finding that thee may be substantial divesification benefits of investing in a divesified housing potfolio. 10

14 house. These benefits ae lowe than suggested by the simple CAPM estimates. Hence, divesification acoss types of houses does not seem to lead to additional divesification benefits. This esult eflects that investing in a potfolio consisting of diffeent types of houses may intoduce additional systematic isk that a homeowne cannot diectly divesify against. Table 3: Housing CAPM models and 3 extensions, , equations (1)-(4) Apatments (=1) Row houses (=) Cone houses (=3) Semi-det. Houses (=4) Detached houses (=5) CAPM models, Equation (1) β 1, = N = 84 β 1 sig. = 6% β 1 > 1 = 45% β 1, = N = 43 β 1 sig. = 14% β 1 > 1 = 45% β 1, = N = 397 β 1 sig. = 10% β 1 > 1 = 53% β 1, = N = 408 β 1 sig. = 10% β 1 > 1 = 55% β 1, = N = 41 β 1 sig. = 10% β 1 > 1 = 48% >0.5 4% θ 1, = 1 = 0.54 θ 1, = = 0.31 θ 1, = 3 = 0.08 θ 1, = 4 = 0.9 θ 1, = 5 = N = 45 θ 1 sig. = 7% othe θ 1 sig. = 16% >0.5 0.% θ 1, = 1 = θ 1, = = 0.7 θ 1, = 3 = 0.8 θ 1, = 4 = 0.18 θ 1, = 5 = N = 406 θ 1 sig. = 10% othe θ 1 sig. = 19% > % Extended CAPM models, Equation () θ 1, = 1 = -0.0 θ 1, = = 0.3 θ 1, = 3 = 0.51 θ 1, = 4 = θ 1, = 5 = N = 377 θ 1 sig. = 8% othe θ 1 sig. = 16% > % θ 1, = 1 = 0.08 θ 1, = = 0.64 θ 1, = 3 = θ 1, = 4 = 0.04 θ 1, = 5 = N = 386 θ 1 sig. = 10% othe θ 1 sig. = 16% >0.5 0.% θ 1, = 1 = θ 1, = = 0.43 θ 1, = 3 = θ 1, = 4 = 0.55 θ 1, = 5 = N = 388 θ 1 sig. = 11% othe θ 1 sig. = 18% >0.5 8% >0.5 1% >0.5 4% >0.5 % >0.5 3% λ 1, = 0.81 λ GDP = λ I = N = 63 λ 1 sig. = 6% λ 1 > 1 = 44% λ 1, = 0.70 λ GDP = λ I = N = 414 λ 1 sig. = 14% λ 1 > 1 = 45% APT models, Equation (3) λ 1, = 0.60 λ GDP = λ I = N = 386 λ 1 sig. = 6% λ 1 > 1 = 50% λ 1, = 0.51 λ GDP = λ I = N = 397 λ 1 sig. = 5% λ 1 > 1 = 50% λ 1, = 0.59 λ GDP = λ I = N = 40 λ 1 sig. = 7% λ 1 > 1 = 51% λ gdp sig. = 10% λ I sig. = 0% λ gdp sig. = 10% λ I sig. = 0% λ gdp sig. = 11% λ I sig. = 1% λ gdp sig. = 11% λ I sig. = 1% λ gdp sig. = 10% λ I sig. = 0.4% >0.5 5% δ 1, = N = 8 δ 1 sig. = 10% δ 1 > 1 = 17% >0.5 1% >0.5 3% >0.5 1% CAPM models based on egional aveage etuns, Equation (4) δ 1, = N = 43 δ 1 sig. = 1% δ 1 > 1 = 13% δ 1, = N = 401 δ 1 sig. = 11% δ 1 > 1 = 13% δ 1, = N = 407 δ 1 sig. = 13% δ 1 > 1 = 14% >0.5 1% δ 1, = N = 414 δ 1 sig. = 14% δ 1 > 1 = 13% >0.5 % > % >0.5 % > % >0.5 1% Notes: This table epots some desciptive statistics of the municipal-specific egessions (aveage beta and -squaed). Heteoskedasticity obust standad eos ae used in each egession. With egad to the CAPM models, N is the numbe of municipal egessions on which the esults ae based. β1 sig. is the pecentage of slope coefficients that ae significant at a 5% significance level acoss municipalities. β1 >1 is the pecentage of slope coefficients that ae in absolute tems lage than 1. R >0.5 is the pecentage of municipal-specific egessions with an R-squaed lage than 0.5. These statistics ae also epoted in the 3 extensions. With egad to the extended CAPM model, othe θ1 sig. is the pecentage of municipalities that have jointly significant coefficients (othe than the type of house unde consideation) at a 5% significance level. In addition, in the APT models, λgdp sig. is the pecentage of significant coefficients on GDP gowth and λi sig. is the pecentage of significant coefficients on the 3-month Euibo. We excluded egessions with an R-squaed of one. In addition, we emoved outlies with egad to the slope coefficients. Most of these coefficients whee lage than 1, not lage than

15 The thid type of estimates, summaized in the thid panel of Table 3, is based on the APT model stated in equation (3). The APT estimates suggest that both the gowth in GDP and the 3-month Euibo inteest ate mainly have a positive effect on house pice changes, ceteis paibus. Howeve, in most cases these estimates ae economically and statistically insignificant. In addition, the esults in panel thee suggest that thee ae less statistically significantly coefficients on the aggegate maket etuns in compaison to the standad CAPM model. Only with egad to apatments, the coefficient on the maket etun seems to be substantially highe. With egad to the divesification benefits of fee tade in the value of the house, ou findings indicate that the divesification benefits ae between 86 and 9 pecent, which is somewhat lowe than the standad CAPM estimates. This esult again eflects the boade intepetation of systematic isk in compaison to the standad CAPM model. Nevetheless, these esults still seem to suggest that the addition of the two contol vaiables does not change ou main finding that the eduction in isk due to investing in a divesified housing maket potfolio may be substantial. 13 Finally, we estimated the CAPM model stated in equation (4). The esults ae epoted in the fouth panel of Table 3. As mentioned, the model in equation (4) is based on the egional aveage etuns fo 40 COROP egions. In compaison to the standad CAPM estimates, the esults in the fouth panel of Table 3 indicate that the significance of the coefficient estimates has inceased. In paticula, between 10 to 14 pecent of the estimates ae significant. Nevetheless, the aveage of the coefficients anges between 0.06 and 0.19, which is lowe than the basic CAPM estimates. This esult implies that a homeowne would need to sell moe futues to hedge the exposue to house pice isk than if the futues would be based on the aveage Dutch housing maket pice. Specifically, a homeowne would need to sell between 5.3 and 16.7 euos in futues contacts pe euo of housing investment, which is highe than the pevious estimate of 1.4 to.0 euos. This esult is also eflected in the fact that in only 13 to 17 pecent of the municipalities the house is an aggessive investment. With egad to the divesification benefits of fee tade in house value, the aveage R- squaed pe type of house suggests that a homeowne could educe the vaiation in house pices by 93 to 96 pecent if this homeowne would invests his housing wealth in a egional potfolio of houses. These esults imply that the aveage hedging effectiveness does not incease if futues ae based on egional house pice indices instead of county house pice 13 As a obustness check, we also estimated the APT models based on the 10-yea Dutch govenment bond yield. In this case, the divesification benefits deceased to 75 to 89 pecent. 1

16 indices (i.e. the basic CAPM R-squaed estimates ae of the same size). In addition, divesification of the housing investment acoss egions does not seem to add to the divesification benefits of fee tade in house value. That is, a homeowne could aleady obtain most of the divesification benefits by simply einvesting his housing wealth in a egional potfolio of houses. Finally, in accodance with the pevious outcomes, the esults seem to suggest that the divesification benefits of fee tade in house value still dominate the isk-educing benefits of house pice futues. 5. Conclusion and discussion House pice isk is high fo homeownes since the typical homeowne concentates the housing investment at a single location only. This is the esult of the indivisibility of the housing investment, in combination with the limited wealth of homeownes, and high tansaction costs in the housing maket. By contast, this pape has investigated the divesification benefits if homeownes could feely tade in the value of the house among each othe. In paticula, we focussed on the question whethe homeownes could educe house pice isk by einvesting the value of the house in a maket potfolio of houses. The esults in this pape suggest that the divesification benefits of fee tade in house value may be substantial. In paticula, ou basic CAPM model estimates have indicated that a homeowne could educe the vaiation in house pice changes by as much as 9 to 96 pecent, depending on the type of house, if he would einvest his housing wealth in a maket potfolio of houses. Divesification acoss types of houses o egions would not lead to additional divesification benefits. Most of the divesification benefits could aleady be obtained by investing in a egional potfolio of houses instead of a county potfolio of houses. These divesification benefits may even be an undeestimate since they ignoe the vaiability of house pice etuns within municipalities. By contast, the esults in this pape indicate that the hedging effectiveness of house pice futues is elatively low. This stategy would educe the vaiation in etuns by only 4 to 8 pecent. Hence, the divesification benefits of fee tade in house value seem to dominate the hedging benefits of futues. As mentioned, Sinai and Souleles (009) have agued that the hedging benefits of homeowneship may explain why deivatives makets have failed to take off. An altenative explanation why deivatives makets ae not in common use today may be that the idiosyncatic isk in local housing makets is simply too high to make hedging with futues an effective hedging stategy (i.e. see De Jong et al., 008). Ou esults seem to cooboate this finding. 13

17 A housing stock maket could facilitate fee tade in the value of the house. Such a maket would allow homeownes to tade stocks based on the value of the home. As a esult, homeownes could educe house pice isk by investing in a divesified maket potfolio (houses of othe homeownes). Altenative ways to obtain the divesification benefits would be that homeownes jointly buy houses, albeit with substantial tansaction costs, o that the homeowne could sell his house fo instance to the govenment o housing copoations. Cuently, a housing stock maket does not exist. Thee may be seveal issues with establishing such a maket. The following discussion biefly summaizes some of these issues, but is not meant to be exhaustive. Fist, the financial liteacy of households may play an impotant ole in the usefulness of a financial (stock) maket based on house pices. In paticula, if homeownes ae unawae of house pice isk, its implications, o if they do not know how to deal with this isk, these makets would not be widely used by homeownes. As a esult, thee may be a potential ole fo govenments/policy makes to incease the awaeness about house pice isk. A second issue elates to the tadability of housing stocks. In paticula, it may be too costly to ceate stocks (stock emission) fo each individual homeowne. Moeove, thee would need to be a citical mass of homeownes that sell housing stocks to make the tade in house value viable. The stocks could, fo instance, be pooled in a fund pe city (municipality/egion) to enhance the tadability of those stocks. A thid poblem may be the owneship stuctue of the house. In paticula, most homeownes use the house as collateal fo the motgage. That is, a motgage povide is also a stakeholde egading the housing investment. Selling the (excess) value on the house would intoduce additional stakeholdes, which could potentially lead to a conflict of inteest between those stakeholdes, fo instance, in case of default. A final issue is that, although fee tade in house value may substantially educe house pice isk, a financial maket clealy cannot fully shelte the homeowne against maket isk (e.g. the subpime cisis). As a consequence, homeowneship may still be associated with a substantial amount of house pice isk even if a housing stock maket would exist. In sum, this pape has emphasized the potential isk-educing benefits of fee tade in house value. Futhe eseach should focus on how financial makets based on house pices could be established and what the effect of such makets would be on housing maket dynamics. 14

18 Refeences Betus, M., Holland, H., Swidle, S., 008. Hedging House Pice Risk with CME Futues Contacts: The Case of Las Vegas Residential Real Estate, Jounal of Real Estate Finance and Economics 37, Caplin, A., Goetzmann, W., Hangen, E., Nalebuff, B., Pentice, E., Rodkin, J., Spiegel, M., Skinne, T., 003. Home Equity Insuance: A Pilot Poject, Yale ICF Woking Pape No Case, K.E., Cotte, J., Gabiel, S., 009. Housing Risk and Retun: Evidence fom a Housing Asset-Picing Model, UCD Geay Institute discussion pape seies. Case, K.E., Shille, R.J., Weiss, A.N., Index-Based Futues and Option Makets in Real Estate, The Jounal of Potfolio Management 19, Cocco, J.F., 000. Hedging House Pice Risk With Incomplete Makets, Pesented at AFA 001, New Oleans. De Jong, F., Diessen, J., Van Hemet, O., 008, Hedging house pice isk: Potfolio choice with housing futues. Woking pape, Tilbug Univesity, Univesity of Amstedam, and Sten Business School NYU. Deng, Y., Quigley, J.M., 008. Index Revision, House Pice Risk, and the Maket fo House Pice Deivatives, The Jounal of Real Estate Finance and Economics 37, Englund, P., Hwang, M., Quigley, J.M., 00. Hedging Housing Risk, Jounal of Real Estate Finance and Economics 4, Flaving, M., Nakagawa, S., 008. A Model of Housing in the Pesence of Adjustment Costs: A Stuctual Intepetation of Habit Pesistence. Ameican Economic Review 98, Foote, C.L., Geadi, K., Willen, P.S., 008. Negative equity and foeclosue: Theoy and Evidence, Jounal of Uban Economics 64, Han, L., 008. Hedging house pice isk in the pesence of lumpy tansaction costs, Jounal of Uban Economics 64, Hinkelman, C., Swidle, S., 008, Tading house pice isk with existing futues contacts. Jounal of Real Estate Finance and Economics 36, 7 5. Ioannides, Y.M., Rosenthal, S.S., Estimating the Consumption and Investment Demands fo Housing and Thei Effect on Housing Tenue Status, The Review of Economics and Statistics 76, Piazolo, D., 010. Deivatives fo the Geman Popety Makets, Gemany Real Estate Yeabook 010. Quigley, J.M., 006. Real estate potfolio allocation: The Euopean consumes' pespective, Jounal of Housing Economics 15, Shille, R., 008. Deivatives Makets fo Home Pices, NBER woking pape Sinai, T., Souleles, N.S., 005. Owne-Occupied Housing as a Hedge against Rent Risk, Quately Jounal of Economics 10, Sinai, T., Souleles, N.S., 009. Can Owning a Home Hedge the Risk of Moving?, NBER woking pape

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