Residential real estate price indices as financial soundness indicators: methodological issues

Transcription

1 Resdental real estate prce ndces as fnancal soundness ndcators: methodologcal ssues Bradford Case and Susan Wachter 1 I. Introducton The purpose of ths conference on real estate ndcators and fnancal stablty s to promote the development of relable, tmely and consstent statstcs on real estate prces n order to support polcy ntatves to promote macroeconomc stablty. The recent volatlty of asset prces and the Asan fnancal crss of 1997 have focused attenton on the role of asset markets and, n partcular, real estate markets n the generaton of fnancal crses and economc nstablty across natons. 2 Wth contagon effects drven by fast and large flows of captal, such natonal crses threaten global fnancal stablty. Hence the need for montorng devces and polcy nstruments to respond to the heghtened potental for asset market nduced global fnancal nstablty. Ths paper focuses on the potental uses of resdental real estate prce ndces as a tool to montor asset market nstablty, and the methodologcal ssues nvolved n ther development. In Secton II we examne how real estate prce ndces can serve as a montorng devce to help mnmse fnancal nstablty. Secton III revews the methodologcal ssues n the development of resdental prce ndces, and Secton IV provdes a dscusson of the avalablty of data n the Unted States to support the development of such ndces. Secton V dscusses what we learn from the prce trends revealed n the ndces. Secton VI concludes. II. The use of prce ndces to montor asset markets and promote fnancal stablty Although there are many possble emprcal methods and data sources for estmatng real estate prce ndces, not all of them can be expected to play an effectve role n promotng fnancal stablty. Before dscussng the methodologcal and data consderatons nvolved n developng a prce ndex, we must consder the functon of a properly constructed real estate prce ndex n montorng asset markets and promotng fnancal stablty. Fundamentally, the prce of any property s equal to the present dscounted value of all future servces (e housng) that wll be provded by that property whle t s owned by ts current owner, plus the present dscounted value of the prce at whch the owner wll be able to sell the property n the future. In general, we would expect the value of housng servces to change only gradually, but the future market prce could change more rapdly. To take ths a step further, the market prce of any property at a gven tme can be defned as the hghest prce at whch the owner would be able to fnd exactly one wllng buyer for that property at that tme. Ths market-clearng prce, however, may fluctuate sharply over tme: because of changes n the avalablty of partcular types of housng servces, because of changes n the cost of fnancng housng purchases, or because of changes n expectatons about future ncreases n the market-clearng prces among other market partcpants. Indeed, a market bubble can be thought of as an ncrease n the market-clearng prces that s justfed only by expectatons that those prce ncreases wll contnue nto the ndefnte future, and not by current or expected changes n the value of housng servces or the cost of fnancng. Although all types of fnancal nstablty can be dsruptve, t s mportant to dstngush these market bubbles from 1 2 The vews expressed are those of the authors and do not represent offcal vews of the Board of Governors of the Federal Reserve System or ts staff. See Mera and Renaud (2000). BIS Papers No

2 what may be termed fundamentally supported fluctuatons n asset values. Both sources of boom-bust cycles are cause for concern, but they may call for sharply dfferent polcy responses. Property markets and real estate prces are nherently subject to booms and busts. One reason for ths s constructon lags: f a surge n demand pushes the prce of exstng property above ts replacement cost, then developers have an ncentve to buld more propertes. But new propertes may take years to complete, and untl the new supply s forthcomng, market-clearng prces wll reman hgh. In the presence of constructon lags, then, prce ncreases effcently reflect the current scarcty of housng servces. Nonetheless, ths ncrease n market-clearng prces wll tend to be followed by a drop once the new supply s forthcomng. Ths cyclcalty n asset values means that lendng at any gven loan-to-value (LTV) rato durng the prce boom - when the demand for constructon fnancng s strongest - may well produce a portfolo of loans wth hgher than antcpated LTVs when asset values drop after supply responds. A second reason for cyclcalty n asset values s the absence of short sellng n real estate markets. Myopc buyers tend to extrapolate prce ncreases nto the future, even when sustaned prce ncreases are not justfed by market fundamentals. In an effcent market, such prce rses would be countered by non-myopc nvestors sellng short (that s, sellng somethng for future delvery that they do not currently own, n the hope that they wll be able to buy t more cheaply later). But, due to the underlyng heterogenety of propertes, there are no organsed futures or optons markets for ndvdual property sales. In markets wth no short sellers, prces wll be drven by myopc buyers so long as the upward trend contnues. Moreover, as Herrng and Wachter (1999, 2002) show, n an economy n whch real estate prces have rsen over a long perod of tme wth no declnes, buyers typcally underestmate the lkelhood of an eventual downturn. That s, nvestors are prone to dsaster myopa, the tendency over tme to underestmate the probablty of low-frequency shocks. 3 Real estate markets are made more vulnerable to fluctuatons because of the role played by the bankng system. As Herrng and Wachter (1999) show, ncreases n the prce of real estate rase the economc value of bank captal to the extent that banks own real estate; thus banks ncrease ther exposure to real estate when prces are rsng. Hgher prces also lft the value of banks own property holdngs and hence ther captal, whch encourages them to relax ther lendng standards. If prces fall, ths process goes vcously nto reverse, and a credt crunch can amplfy the mpact of fallng prces. Moral hazard may also contrbute to a bank s supply of captal to real estate, exacerbatng booms and busts. To the extent that bank managers salares and bonuses are based on reported short-term profts wthout adjustment for reserves aganst shocks, the lne offcers who are n the best poston to assess such dangers wll be rewarded for dsregardng them (Pavlov and Wachter (2004)). Moreover, Pavlov and Wachter (2003) show that, due to compettve pressures n the bankng ndustry, all managers wll be pushed to underprce the rsk of real estate loans, and, addtonally, bank shareholders themselves wll ncentvse such behavour. In addton to problems of moral hazard, poor nformaton and nadequate analyss of real estate rsk contrbute to the vulnerablty of the bankng system. Banks and ndvdual managers, besdes beng poorly ncentvsed, have lttle data on whch to base careful analyss of future real estate prces. The property value apprasal process s based on observng the prces of comparable propertes to estmate the market value of propertes (and therefore LTV ratos). Whle lendng decsons would deally be made on the bass of long-term expectatons about the market value of the property throughout the lfe of the loan, the observed transacton prces of comparable propertes are marketclearng prces, subject to bubbles and other sources of short-term fluctuaton. Moreover, prces of comparables cannot be used for apprasal purposes untl after the transacton s closed, whch means that prce ndces based on apprasals generally lag actual movements n real estate prces. Real estate prce ndces can serve n two ways to reduce boom-bust cyclcalty n asset value markets, and the attendant cyclcalty n the bankng system. Frst, to counter the tendency for banks and apprasers to underestmate LTV ratos by basng them on short-term real estate prce booms (whether nduced by bubbles or not), ndces of current market-clearng prces can be compared to measurements of what mght be called long-term property values. Long-term value, for example, mght be thought of as the (relatvely stable) value of housng servces, plus an average over the range 3 See Tversky and Kahneman (1982) and Guttentag and Herrng (1986). 198 BIS Papers No 21

3 of future non-bubble market values (all dscounted to present value). One advantage of estmatng such long-term value s that t could potentally prevent banks from fnancng property transactons or constructon based on unrealstc expectatons about future market prces. A major concern, however, s that t s far more dffcult to construct ndces of long-term value than of current market value. Ths has consequences for the avalablty of credt. Transactons occur only at the current market-clearng prce: for example, f governments mandate that sales cannot occur at any prce above (or below) the long-term value, then sellers (or buyers) wll generally refuse to sell (buy) f the current market-clearng prce dffers from the long-term value. 4 In any case, the tendency for banks to ncrease exposure to real estate by lberalsng LTV ratos durng real estate booms can be countered. Second, extreme volatlty n the prce ndex, or extreme dfferences between the ndex of current market-clearng prces and long-term value, can functon as a warnng that a market bubble has occurred, perhaps requrng a dfferent publc polcy response. Whle a dscusson of the feasblty of developng such methodologes s a subject for another paper, t may be useful to offer some suggestons on how these analyses could be mplemented. For ths to work, for example, methodologes could be developed to estmate expected volatlty, or the extent of devatons from long-term equlbrum values. Estmaton methodologes could be based on housng cycles or on ratos that are derved from such models. Addtonally, under smplfyng assumptons, ratos of prces to rents could be used to uncover prevalng prce change expectatons (gven real nterest rates), whch can be compared to model-generated expected prce changes. Generally, actual prce realsatons could be compared to model-specfed prce outcomes through smulaton based on assumptons on supply and demand functonal forms. Whle there are many possble housng market models and the specfcatons would vary wth the underlyng characterstcs of the economy, all emprcal models that are desgned to track current asset prce realsatons as compared to longer-run outcomes must frst dentfy and measure the current market asset prce of housng. As the followng detals, ths s not a small task, conceptually or practcally. Fnally, t s worth notng that moral hazard and scale economes suggest that the development of real estate prce ndces s an approprate exercse of the government s regulatory functon. Banks cannot be reled on to construct market-wde prce ndces, both because they do not ndvdually have adequate data and because ther ncentve structures may oppose the collecton of relable market data. Furthermore, technology and data requrements mean that there are lkely to be strong economes of scale n the development and mantenance of prce ndces, whch suggests the value of centralsed prce ndex estmaton. Whle there s certanly a place for prvate sector estmaton of real estate prce ndces, the goal of fnancal stablty may well best be served by the development of approprate prce ndces at the central government level. III. Methods used to construct resdental real estate prce ndces As noted above, there are many possble emprcal methods and data sources for estmatng real estate prce ndces, and selectng the most approprate method and data must depend n large part on the functon to be served: montorng asset market nstablty and promotng fnancal stablty. Each methodology s usually best suted to a certan type of applcaton. In ths secton we dscuss the dfferent emprcal methods avalable, evaluatng the extent to whch each method can be expected to further the goal of fnancal stablty. Four methods are commonly used to comple resdental real estate prce ndces. The most straghtforward s smply the average or medan prce 5 durng each tme perod. For example, n the Unted States, the Natonal Assocaton of Realtors publshes an ndex gvng the medan prce of exstng sngle-famly resdental propertes that transacted n each quarter for each metropoltan 4 5 Some governments currently attempt to embody long-term market value n prce ndces constructed usng judgment of local apprasers and assessors. Whle ntutvely nterestng from a polcy perspectve, t s dffcult to judge how well such procedures have worked. Moreover, apprasers n the Unted States and Royal Chartered Surveyors are requred to estmate the current market-clearng prce, rather then long-term value. Our dscusson focuses on prce (at transacton), but ndces may also be compled on the bass of value (at or between transactons). Below we dscuss the choce between usng transacton prces and values. BIS Papers No

4 statstcal area (MSA) n the Unted States The medan prce s generally preferred to the average because the dstrbuton of prces s sharply skewed, so that fluctuatons n sales volume among expensve propertes would have a strong effect on average sellng prce but a muted effect on medan sellng prce; for some applcatons, however, average prce mght be preferable. The data requrements for ths method are mnmal: smply the prces at whch all (or a representatve sample of) propertes transacted durng the tme perod. The major problem wth ths method, however, s qute substantal: t fals to control at all for changes n the qualty of the propertes whose prces were observed n each perod. Qualty, of course, tends to mprove over tme as new propertes are constructed, older propertes are demolshed, and exstng propertes are renovated; because of ths, an average- or medan-prce ndex tends to substantally overstate the ncrease n prce for a constant-qualty property, or for any exstng (and deprecatng) property. Moreover, the sample of propertes that transacts n a gven tme perod s not constant over tme; because of ths, an average- or medan-prce ndex tends to overstate prce ncreases when all that s happenng s that relatvely expensve propertes are overrepresented among transactons, and understate prce ncreases when relatvely nexpensve propertes are overrepresented. A second reasonably straghtforward technque to track property prces, the representatve-property method, s mplemented by defnng a representatve property and then recordng n each perod the prce (or value) of a property conformng to the specfed characterstcs. The shelter component of the US consumer prce ndex (CPI) essentally proceeds n ths way, as do some propretary ndces such as the Natonal Real Estate Index publshed by Global Real Analytcs. The only data tem that s actually collected s the prce of the representatve property n each tme perod. In order to mplement ths method, the data collector must observe all of the characterstcs used n defnng the representatve property n order to select one conformng to the defnton. The major problem wth ths method s that data ponts may not be fully comparable across markets or over tme, because of dfferences among data collectors n subjectvely nterpretng the defnton of the representatve property and applyng that defnton to choose a representatve property. A second problem s unmeasured qualty change: specfcally, qualty mprovements that are not captured by the defnton of the representatve property. For example, f a property s defned wth respect to locaton, lot sze, lvng space, and number of rooms but not wth respect to major amentes such as central ar condtonng, then any ncrease n the prevalence of those amentes wll show up mproperly as an ncrease n the prce ndex rather than properly as an mprovement n qualty. Fnally, because the method focuses on the prce of only one property (the representatve), t does not take advantage of nformaton contaned n the prces of all other propertes; n fact, n extreme cases t may not reveal the movements n the general prce level f, for whatever reason, the representatve property s prce does not respond n the same way. In order to avod the problems nherent n the average-/medan-prce and representatve-property methods, economsts estmate prce ndces usng hedonc-prce models. These models postulate that the transacton prce of any gven property s a functon of the tme perod n whch t transacted as well as ts hedonc characterstcs - that s, the physcal features of the house or lot, and the features of ts locaton and neghbourhood, that affect the prce at whch t transacts. If we know the hedonc functon, then regresson analyss can be used to estmate the parameters of ths functon. For example, a common hedonc-prce functon s P t = αx β1 β2y +γ1t 1+γ2T γntn e or, n logs, InP t = α + β 1 lnx + β 2 Y + γ T 1 + γ 2 T γ n T n where P t s the transacton prce of property durng tme perod t ; X and Y are hedonc attrbutes of the property (wth X measured contnuously - say, square feet of lvng space - and Y measured dscretely - say, presence of central ar condtonng); T τ are dummy varables ndcatng whether the transacton took place durng tme perod τ; and α, β j and γ τ are the parameters to be estmated. In partcular, the seres of parameters γ τ s the prce ndex. The hedonc-prce method offers several advantages over the average-/medan-prce and representatve-property methods. Frst, and most mportantly, the hedonc-prce method controls for qualty change: specfcally, f there has been any change n the attrbutes measured X and Y - ether because the qualty of ndvdual propertes has changed or because dfferent-qualty propertes are more lkely to transact - then ths qualty change wll be reflected n the hedonc measures rather than n the parameters (ncludng the prce ndex). Ths s a great advantage over the use of the medan and average prce. Compared to the representatve-property method, the hedonc-prce method does not 200 BIS Papers No 21

5 requre subjectvely nterpretng the defnton of the representatve property or applyng that defnton to choose a representatve property, nor does t fal to make use of data from other propertes. On the other hand, the method does have some dsadvantages as well. The data requrements are much more onerous than for the average-/medan-prce method, 6 as the analyst should have data on all of the hedonc attrbutes of the property, as well as ts prce, at the tme of the sale. Ths method potentally shares the problem of unmeasured qualty change; f the hedonc measures do not capture amentes that mproved over tme, then any ncrease n the prevalence of those amentes wll show up mproperly as an ncrease n the prce ndex rather than properly as an mprovement n qualty. Another dsadvantage s the problem of determnng the correct model specfcaton. The hedonc-prce functon must be specfed correctly - that s, the analyst must use the correct functonal form and nclude all relevant hedonc characterstcs (e must not have any omtted varables) n order to generate unbased estmates of the prce ndex. 7 Moreover, the parameters on the hedonc-prce attrbutes (β j, called the mplct market prces of the attrbutes) must not have changed over tme, or f they have, then that must be ncorporated nto the functonal form. Any volaton of these condtons - ncorrect functonal form, omtted varables or changng parameters - theoretcally wll result n based estmates of the prce ndex. In practce, however, t appears that the hedonc-prce method s qute robust to reasonably mnor volatons of these condtons: for example, t appears that the estmated prce ndex wll be farly close to the true prce ndex as long as several of the most mportant hedonc attrbutes (eg number of bathrooms) are ncluded. Thus t seems, n practce, that the major dsadvantage assocated wth the hedonc-prce method s the cost of data collecton. 8 The onerous data requrements of the hedonc-prce method (as well as of the representatve-property method) have encouraged analysts to use a smpler method derved from hedonc-prce models, called the repeat-sales method. Ths method takes advantage of the fact that when a gven property transacts twce, many or most of the hedonc attrbutes of that property wll not have changed at all between transactons. To the extent that ths s true, the analyst need not collect data on the level of each hedonc attrbute at the tme of ether sale; t s enough to know that the attrbute has not changed. In these cases, the change n prce of the property between transactons can be expressed as a smple functon of the tme perods elapsed between transactons. The cost and ease of mplementaton advantages of the repeat-sales method have made t the prce ndex methodology of choce for large-scale applcatons: for example, prce ndces for sngle-famly resdental propertes n several hundred US metropoltan areas (as well as at the natonal, regonal and state levels) are publshed quarterly by both Fredde Mac and the Offce of Federal Housng Enterprse Oversght (OFHEO). Nonetheless, as dscussed further below, there are mportant measurement dsadvantages n the use of such ndces. Chef among these dsadvantages s the need for frequent transactons. The repeat-sales methodology can only be used n markets where propertes are transacted frequently and plenty of sales data are avalable. In western Europe, for example, the repeat-sales methodology s not useful gven the small number of housng transactons. Moreover, t should be noted that repeat-sales prce ndces need to be combned wth ntal prced hedonc ndces to compute comparable prce levels across markets. The repeat-sales model s derved from the hedonc-prce model by expressng the rato of the prces for two transactons of the same property as the rato of the rght-hand-sde hedonc functons for those two transactons: But no more onerous than the representatve-property method: although only the prce of the representatve property s actually recorded, nformaton on the full set of hedonc characterstcs should be used to defne and dentfy a representatve property. Halvorsen and Pollakowsk (1981) addressed the dffculty of selectng the correct functonal form for a hedonc prce model. Meese and Wallace (1991) proposed a non-parametrc method for estmatng the mplct market prces of hedonc attrbutes n order to avod ths problem. Constant-qualty methodologes are deal for many uses and applcatons such as attemptng to dentfy a bubble n housng markets. In ths type of analyss, the pure prce sgnal s what should be dentfed and analysed n an attempt to see f prcng has become rratonally hgh. Nonetheless, a fnancal nsttuton attemptng to mark to market LTV ratos on a portfolo of mortgages would not want to use a constant-qualty methodology snce mprovements n qualty on a collateral property are real mprovements n value that should be consdered. BIS Papers No

6 P P αx e β1 β2y +γ1t 1+γ2T γntn t = β1 β2y +γ 1T 1+γ2T γntn t αx e or, n logs, P X ( Y Y ) + γ ( T T ) + γ ( T T ) γ ( T T ) t ln = β1 ln + β n n n. Pt X Because X = X and Y = Y, ths can be smplfed to Pt ln = γ ( T1 T 1) + γ 2( T 2 T 2 ) γ P t ( T T ) 1 n n n where P t s the transacton prce at the tme of the prevous sale; X and Y are the hedonc attrbutes of the property at the tme of the prevous sale; T τ are dummy varables ndcatng whether the prevous transacton took place durng tme perod τ; and the seres of parameters γ τ s the prce ndex. Two ponts are worth notng about the rght-hand sde of ths equaton. Frst, the expressons T n T n take the values 1 durng the tme perod of the frst transacton, +1 durng the tme perod of the second transacton and 0 otherwse. Second, any property that sold at least twce 9 n dfferent tme perods can be ncluded n the analyss, but f all transactons of that property occurred durng the same tme perod, then the property must be dropped from the analyss because all terms on the rght-hand sde wll have the value 0. As noted, the major advantage of the repeat-sales model s that t requres lttle data collecton. Ths apples so as long as t s known that none of the relevant hedonc characterstcs of the property have changed between transactons. However, t s easy to overestmate ths advantage n practce, because an analyst must have some relable way to determne whether, ndeed, the property s characterstcs have remaned constant. Ths generally means that the practcal data requrements of the repeat-sales model are qute smlar to those of the hedonc-prce model or, alternatvely, the potental that an ndex ncrease s smply due to qualty ncreases cannot be determned. In practce, analysts generally assume that hedonc attrbutes have not changed between transactons, and ths assumpton of course greatly reduces the data collecton burden. Ths assumpton, however, s not generally true, and for ths reason the unmeasured qualty change ntroduces an unknown postve bas nto the estmated prce ndex, thus undermnng ts use n montorng unsustanable prce ncreases. An advantage of the repeat-sales model s that t automatcally controls for all hedonc characterstcs that remaned unchanged between transactons, whereas the hedonc-prce model controls only for those that are measured. Because of ths, the repeat-sales method makes more effcent use of the nformaton contaned n repeat transactons of a gven unchanged property. There s a major cost assocated wth ths, however: because t uses nformaton only on transactons of those propertes that sold at least twce durng the study perod (and remaned unchanged between transactons), the repeat-sales method gnores a very large amount of potental nformaton from transactons of propertes that sold just once durng the study perod (or that changed between transactons). The number of property transactons gnored n ths way vares wth the length of the study perod and the level of market actvty, but generally s the great majorty of avalable transactons. 10 Another dsadvantage of the repeat-sales model s the changng-parameters problem dscussed above n connecton wth hedonc-prce models: the parameters on the hedonc-prce attrbutes (β j, the mplct market prces) must not have changed over tme, or f they have, then that must be ncorporated nto the functonal form of the hedonc-prce equaton. In the standard repeat-sales formulaton, however, there s no way to modfy the functonal form of the equaton to ncorporate 9 10 If a property has transacted more than twce durng the study perod, then each observaton (transacton par) on that property must be weghted to correct for collnearty n the dsturbance terms. See Baley et al (1963) and Palmqust (1982). Moreover, propertes may be more or less lkely to transact dependng on whether prces are ncreasng rapdly or slowly (or declnng), n whch case a repeat-sales prce ndex could potentally be based. Note that ths problem of sample selecton bas would also exst for hedonc-prce methods, but would be much less serous even than for repeat-sales methods. 202 BIS Papers No 21

7 changes n mplct market prces; nstead, the effect of the changed mplct market prces wll be mproperly reflected n the prce ndex. 11 Ths dsadvantage, together wth the other shortcomngs of the repeat-sales method - falure to use nformaton from propertes that transacted just once, (measurable) changes n hedonc attrbutes, and changes n mplct market prce parameters - motvated the development of a hybrd model that combnes attrbutes of both the repeat-sales and the hedonc-prce method. 12 The essence of hybrd models s that they stack repeat-sales and hedonc models, and then estmate the two models mposng a constrant that estmated prce changes over tme are equal n both models. In effect, such methods are weghted averages of the hedonc and repeat-sales methods. The evdence suggests that repeatedly sold propertes may dffer n unobserved ways from other propertes, n whch case the stackng method nduces measurement error. Whle such ndces, unlke hedonc or repeat-sales ndces, do use all avalable nformaton, Case et al (1991) do not fnd clear effcency gans from usng the hybrd model nstead of the hedonc approach. IV. Types of prce data wth whch to create resdental prce ndces In addton to the wde varety of emprcal methods avalable, there are also many dfferent types and sources of data that could be used to construct real estate prce ndces. As we dscuss n ths secton, however, few of these data sources would support the development of relable prce ndces that can be expected to promote the goal of fnancal stablty. In the Unted States, there are several sources of data on real estate prces or values, some collected by government agences and provded to the publc free of charge, others collected prvately and kept prvate or offered for sale. The Census Bureau of the US Department of Commerce, for example, provdes data on sales prce, and medan and average prces, on an annual and quarterly bass, for New Houses Sold and another set of prce ndces for Medan Prces of Exstng Famly Dwellngs. 13 The major lmtaton of these data seres s the overstatement of house prce apprecaton, because they do not account for the changes n qualty that occur over tme. The Census also constructs, based on new constructon, a Constant-Qualty Prcng Index, snce 1978, although ts value s lessened due to ts geographcal lmtatons. A set of statstcal models relatng sales prce to selected standard physcal characterstcs of house unts s used to derve the average prce for a constant-qualty unt. Generally, the geographc dstrbuton of these ndces s lmted to an aggregate ndex of the Unted States and the four major census regons. 14 An ssue to be consdered when prces are based off new constructon s where the new constructon occurs. Because new constructon s lkely to occur on the frnge of urban areas where supply s elastc, such ndces may underestmate property prce apprecaton. For example, prce apprecaton of newly constructed homes n suburban Rhode Island or Massachusetts would not be comparable to the apprecaton rates of condomnums n downtown Boston. New constructon methodologes therefore may not pck up the effects of land scarcty n a market, and may tend to underestmate overall market prce apprecaton for ths reason. A second US government source for house prce data, the CPI publshed by the Bureau of Labor Statstcs of the US Department of Labor, s constructed usng the representatve-property method. 15 The largest part of the CPI s housng seres s n the shelter category, 16 whch covers rent of prmary Shller (1993) showed that a generalsaton of the standard repeat-sales formulaton, however, permts the estmaton of a separate prce ndex for each hedonc attrbute. See Case and Qugley (1991). These data are constructed by the Natonal Assocaton of Realtors (NAR), who also release a quarterly, qualty-unadjusted seres for a panel of large MSAs based on medan prces from the local Board of Realtor Multple Lstng Servce. In addton, the decennal census data record house prces and rents, and publsh medan values for MSAs and even smaller jursdctons. See for more nformaton. Other parts nclude the prce of household furnshngs, applances, utlty servces, etc. BIS Papers No

8 resdence 17 and owner s equvalent rent (far and away the most heavly weghted tem n the overall seres). Owner-equvalent rent s constructed from data provded by homeowners themselves. Homeowners are asked what ther unt would rent for. 18 Ths methodology approprately calculates changes n owner user costs and, by desgn, t does not measure changes n house prces or values. As dscussed above, change n the owner-equvalent rental component of the CPI can be compared to value change as an ndcator of asset prce nflaton relatve to changes n the prce of the underlyng stream of housng servces, but t cannot be used to measure house prce nflaton. A thrd source of prce data s mortgage transactons, whch are used for repeat-sales prce ndces provded by OFHEO. As federal regulator of the two government-sponsored enterprses (Fredde Mac and Fanne Mae), OFHEO has access to the naton s largest database of mortgage transactons, over 23 mllon repeat transactons on conformng, conventonal sngle-famly loans nsured by the GSEs. Both OFHEO and Fredde Mac make quarterly seres, organsed by census dvson, state, MSA, or natonal, avalable on ther webste, free to the publc. The ndces are easly downloadable n Excel or text format, generally two months after quarter-end. The natonal, census dvson, and state seres are avalable back to 1975, but the MSA seres have dfferent startng ponts because an MSA seres s only publshed f at least 1,000 observable transacton pars exst n the area. One advantage of these data s the hgh frequency, but ths also leads to frequent revsons. Each quarter, recent mortgage transacton data from the GSEs are combned wth past data and calculatons are then performed on ths updated dataset. The ndex s created by lookng at all propertes whch have been sold at least twce and comparng the two sale prces usng a modfed Case-Shller method. 19 A dsadvantage of the OFHEO and Fredde Mac seres, besdes those dscussed above that are generc to repeat-sales prce ndces, s ther lmted geographcal coverage. Prvate analysts, such as Case and Shller (1989, 1990), present basc results for an addtonal but stll lmted number of locatons. The prvate frm Fserv CSW (formerly Case-Shller-Wess) and a collaboraton of the research departments of Fanne Mae and Fredde Mac have produced such ndces for a wde range of MSAs and smaller areas; however, the small area ndces are propretary and not readly avalable for research purposes. Most of the dscusson n ths paper has been phrased n terms of a prce ndex based on property transactons, but that s not the only type of data that can be used (or that s commonly used) to comple resdental real estate prce ndces. The advantages of usng actual sales prces from property transactons are, frst, that sales prces (from arm s length, non-coerced transactons) represent the most relable ndcator of the actual market value of any gven property; and, second, that data on sales prces may already be readly avalable f they are collected for the admnstraton of real property taxes, transfer taxes, deed recordng fees, or other publc purposes. The dsadvantages of usng sales prces from property transactons are, frst, that durng a gven study perod only a fracton (generally a small fracton) of all propertes wll have transacted even once; and, second, that f some propertes are more or less lkely to transact dependng on whether prces are ncreasng rapdly or slowly (or declnng), then the use of transacton prces may ntroduce sample selecton bas nto the estmaton of the prce ndex. These dsadvantages appear to be mnor compared to the qualty advantage of data from actual market transactons. 20 It s also possble, however, to comple resdental real estate value ndces from observatons on what s beleved to be the underlyng market value of a gven property, as opposed to the prce observed (only) when that property transacts. Perhaps the most straghtforward source s estmates of the market value of each property that are recorded for the purposes of assessng real property taxes, Shelter also ncludes lodgng away from home, housng at school, excludng board, other lodgng away from home ncludng hotels and motels, and tenant s and household nsurance. From 1987 to 1998, CPI data collectors gathered nformaton from the owners to calculate an approprate ntal, mplct rent. Changes for smlar (based on structure and attrbutes) renter-occuped unts were then appled to the ntal value to calculate changes n owner-occuped mplct rents. Snce 1998, the rent ndex of the survey has smply been reweghted to rents on the CPI Housng Survey. For owner-occuped multfamly rental propertes, prce ndces that are based on transactons may reflect varaton n lqudty over the busness cycle, whch affects the ease wth whch owners are able to sell propertes. Fsher et al (2003) propose a constant-lqudty prce ndex method, and fnd that movements n the constant-lqudty ndex tend to lead movements n a transacton-based ndex. 204 BIS Papers No 21

9 whch are mposed almost unversally n the Unted States. Real property tax assessment records are readly avalable n any jursdcton that mposes the real property tax, and they are establshed regularly, generally every year. Unfortunately, real property tax assessment records are generally consdered to be of very poor qualty. Even f they are updated annually, the updatng process may bear lttle relaton to changes n the market prce level, for several reasons. Frst, for smplcty most jursdctons adjust the assessed values of all propertes wthn the jursdcton by the same factor, regardless of whether prces n parts of that jursdcton have ncreased more or less rapdly than the average. Second, the adjustment factor s set through a poltcal process that need not reflect actual market fluctuatons. Thrd, assessed values for ndvdual propertes may be set closer to market prces only when those propertes transact; ndeed, n some jursdctons (such as Calforna) assessed values may be explctly prevented from adjustng to the same extent as market prces. Fnally, homeowners are much more lkely to challenge the estmated values on whch ther property tax assessments are based when those values have ncreased sharply, so property tax assessment records generally tend to understate the actual pace of property value ncreases. For these reasons property tax assessments are rarely, f ever, used as resdental real estate value ndces n themselves. A much more commonly used source of market values s records from prvate apprasals, whch are generally conducted n connecton wth mortgage transactons - whether purchase-money mortgages upon a property transacton, or mortgage refnancngs. Indeed, n the Unted States the ndces publshed by Fredde Mac and OFHEO both nclude apprased values from records of refnanced mortgages purchased by Fredde Mac (and, for the OFHEO ndex, Fanne Mae). The qualty of prvate apprasals s probably much hgher than the qualty of property tax assessment records, but apprasals may stll dffer sharply from underlyng market values because of the subjectvty of the apprasal process, partcularly f the subject property dd not transact at the same tme and there were few transactons of closely comparable propertes durng the same tme perod. Moreover, apprasal-based prce ndces may suffer from sample selecton bas, especally snce homeowners may be more or less lkely to refnance ther mortgages f property values are ncreasng rapdly. 21 For these reasons, economsts have found that prce ndces based on apprasal records tend to be smoother than prce ndces based on transacton records and tend to msrepresent the tmes at whch market prces reach ther peaks or troughs. Also for these reasons, n the Unted States both Fredde Mac and OFHEO are consderng deletng apprasals that were conducted n connecton wth mortgage refnancngs from the computaton of ther prce ndces. Another source of nformaton on property values s records on lstng prces of propertes offered for sale: for example, varous local multple lstng servce (MLS) databases n the Unted States have been used to construct value ndces. The advantages of these data are that (1) lstng prces are establshed wth the assstance of real estate agents, who may have especally good judgment regardng the value that would be assgned to each property n a well functonng market, and (2) the number of propertes lsted for sale durng any tme perod s even greater than the number of property transactons. The dsadvantages of these data, however, are closely related to the advantages. Frst, lstng prces may dffer sharply from underlyng market values, partly because nether real estate agents nor homeowners may be good judges of market value and partly because they may n fact have ncentves not to equate the lstng prce wth the market value. Second, propertes wth partcularly low lstng prces relatve to market value can be expected to transact quckly, whle propertes wth partcularly hgh lstng prces relatve to market value can be expected to reman on the market for a long tme and perhaps never transact. For these reasons lstng prces are generally not consdered a relable source of market value data. Nonetheless, the underlyng data collected on assessments and lstng prces have themselves been used n estmaton of hedonc ndces (Clapp and Gacotto (1992)). Moreover, the underlyng data on sales transactons, ncludng prces, date of sale, and housng attrbutes, collected by the MLS and by muncpaltes, are potentally valuable for the constructon of prce ndces. Apprasal and assessor 21 In addton, some property apprasals may be motvated not by mortgage transactons but smply by the observaton that the pace of market prce ncreases seems to have changed sgnfcantly. Ths s much less common among (sngle-famly) resdental propertes than among commercal propertes (ncludng multfamly resdental propertes), but should be recognsed because prce ndces based on apprasals that are motvated n part by sharp ncreases, or declnes, n the general property prce level can be expected to suffer from sample selecton bas. BIS Papers No

10 agences are movng towards usng these data for statstcal-model based prce estmaton. Assessors are ncorporatng hedonc methodologes n computer-asssted mass apprasal (CAMA) and apprasers are usng automated valuaton methods (AVMs) for desk revew apprasals and for mortgage underwrtng. Thus lenders and muncpal authortes are ncreasngly makng use of statstcal methods to estmate the market value of homes; these technologes also have the potental to generate standardsed 22 hedonc local area resdental prce ndces. A fnal source of market values s smply a survey of homeowners requestng that they assess the value of ther own propertes. The Amercan Housng Survey, for example, records owner-assessed property values, and these values have been used to construct value ndces. 23 In prncple, ownerassessments can provde a useful source of market value nformaton, as homeowners (1) are partcularly knowledgeable about the condton and amentes (structural and locatonal) of ther propertes, and (2) often observe market prces of comparable neghbourng propertes. However, property owners are not necessarly good judges of the value that would be assgned to ther propertes by a well functonng market. Indeed, economsts have found that homeowners tend to overestmate the market values of ther propertes, and tend furthermore to underestmate the rate of ncrease n the market values of ther propertes (Kel and Zabel (1999)). For ths reason owner assessments are generally not consdered a relable source of market value data wth whch to construct property value ndces. Nonetheless, these data have been used by researchers to construct hedonc ndces for the Unted States. For example, Malpezz et al (1980) used AHS data from the 1970s to construct constant-qualty ndces for a sample of MSAs. Ths work was subsequently updated and expanded by Thbodeau (1992, 1995). V. What stores do the US resdental real estate prce ndces tell? The most mportant story told by resdental prce ndces about the US resdental real estate market s that, n the long run, housng prce ncreases n the Unted States have tracked ncreases n the overall prce level qute closely. Graph 1 below shows the overall CPI, CPI Rent, CPI Owner-Equvalent Rent and Census Constant-Qualty prce ndces from 1979 to The growth rates of these ndces over ths roughly 20-year perod were smlar; however, the close relatonshp between housng prces and overall prces often does not hold over short tme perods. For example, n Graph 2, year-over-year growth rates n the CPI, CPI Housng and Census Constant-Qualty ndces are shown from 1997 to In sx of the seven years snce 1997, the apprecaton n the CPI Housng ndex has exceeded the growth n the overall CPI ndex. However, usng the growth of the Census Constant-Qualty ndex as a measure, the housng prce growth rate was sgnfcantly hgher than the growth of the overall CPI and CPI Housng ndces only n In order to determne f there s currently a bubble n US resdental real estate, t s mportant to look not only at housng prce ncreases, but also at rent ncreases. If house prces are apprecatng rapdly, ths does not necessarly mply that a bubble exsts f rent prces are ncreasng just as rapdly, snce consumers are ratonally prcng ncreasng rents nto owner-occuped housng unts. The data n Graphs 1 and 2 do not demonstrate the exstence of a bubble n US resdental real estate markets. Over the past seven years shown n Graph 2, rents and constant-qualty apprecaton have been very smlar, and n most years apprecaton n rents (CPI ndces) has actually been hgher than growth n constant-qualty home prces. Whle the rato of CPI Rent ndex to the housng prce, usng the Constant-Qualty ndex, does not show any declne, some prvate data collected on rents do mply declnng rents over 2001 and 2002 that, when coupled wth ncreasng constant-qualty house prces, could lend some strength to arguments that a bubble does exst. Prvate data compled by REIS, RERI and others show rents declnng over 2001 and These ndces nclude only effectve rents on newly leased propertes, and do not consder rental ncreases on propertes whch are already Some muncpaltes collect nformaton on numerous housng attrbutes, others on few. However, the use of geographcal nformaton, whch s avalable for all muncpaltes, can substtute for an nclusve lst of attrbutable varables. To supplement the decennal census, the Commerce Department releases the Amercan Housng Survey, started as an annual survey n 1973 and changed to a bannual one n the early 1980s. Currently, the AHS covers about 50,000 housng unts throughout the Unted States. 206 BIS Papers No 21

11 leased. These data therefore may more accurately reflect the current state of rental markets, rather than the smoothed CPI ndces that nclude rents and escalatons on exstng leases. Graph 1 CPI vs Census Constant-Qualty, CPI Rent and CPI Owner-Equvalent Rent (1983 = 1.0) CPI Owner-Equvalent Rent Census Constant-Qualty CPI Rent CPI Graph 2 Annual growth n overall CPI, CPI Housng and Census Constant-Qualty Indces, September-September (n per cent) CPI CPI Housng Census Const-Qualty BIS Papers No

12 It should also be remembered that the recent acceleratng growth n constant-qualty prces shown n Graph 2 above s wthn a range of ncreases that would be predcted gven the tremendous declnes n mortgage rates experenced over the past 10 years. Whle some observers consder the effects of declnng nterest rates on housng prces a bubble, t s mportant not to confuse a bubble wth a commonly experenced cycle. Bubbles usually refer to rratonal asset prcng, but consumers have been completely ratonal to bd up home prces as nterest rates have declned. However, ths s not to say that housng prces wll not experence some weakness as the cycle turns, and consumers bd lower amounts due to ncreasng nterest rates. Whle headlne prce ndces, such as means and medans, have shown rapd growth n recent years, t s mportant to remember that these numbers are nfluenced by ncreasng qualty of housng, and are not representatve of pure prce nflaton. In Graph 3, a prce ndex for new homes sold s compared to a constant-qualty prce ndex. The much greater ncrease n new home prces when compared to the ncrease n constant-qualty prces shows that Amercans are ncreasngly demandng much better qualty n ther housng, and that ths demand s drvng overall housng transacton prces hgher. However, one must remember that ths qualty-related apprecaton s not a bubble, snce consumers are payng more for a better product, a completely ratonal economc behavour. Graph 3 Prce ndces for new homes sold vs constant-qualty prce ndex New homes sold Constant-qualty Many people feel that repeat-sales ndces control for changes n housng qualty, but, n realty, ths s not the case. The qualty of a sngle house s not statc between transactons, snce owners may renovate, expand, or make other qualty mprovements to the property. The data shown n Graph 4 bear out ths hypothess. In the graph, the OFHEO repeat-sales ndex s compared to a constantqualty ndex. Snce 1985, the OFHEO ndex has ncreased much more rapdly than the constantqualty ndex, showng that repeat-sales ndces do not fully control for changes n housng qualty snce owners may mprove qualty between transactons. Whle ths does show real postve nvestment n the naton s housng stock, ths s not to say that ths nvestment n qualty wll contnue ndefntely. If nterest rates ncrease, demand for real estate may declne, and the current nvestment n real estate qualty may prove excessve. 208 BIS Papers No 21

13 Graph 4 OFHEO repeat-sales ndex vs constant qualty ndex, OFHEO repeat-sales ndex Constant-qualty ndex One mportant caveat to the above analyss s that all the ndces used were natonal, and whle they do not seem to mply the exstence of a natonal resdental real estate bubble, t does not follow from ths that bubbles do not exst n any ndvdual regonal markets. Real estate markets are regonal n nature, and anyone who wshes to analyse the state of the market should rely more heavly on regonal prce ndces of nterest rather than aggregated natonal ndces. Indvdual markets or regons can have vastly dfferent current stuatons and hstorcal experences wth real estate prcng and apprecaton than does the naton as a whole. VI. Concluson The organsers of ths conference have recognsed the fundamental connecton between real estate markets and fnancal stablty, and therefore the need for prudental supervson and montorng of real estate markets. Because banks are exposed to cyclcalty n real estate markets, and because banks ncentve structures may lead them to exacerbate boom-bust cycles n real estate markets, fluctuatons n real estate prces have the potental to stran fnancal stablty and even to jeopardse entre fnancal systems. In countres n whch banks play a domnant role - such as Japan, where banks hold some four ffths of total assets - the consequences for the real economy can be severe. In partcular, for several reasons, banks are lable to ncrease ther orgnaton of real estate loans at the same tme that short-term, market-clearng asset prces are at ther peaks. As asset prces revert to ther longer-term values, however, the result s that banks hold portfolos of loans wth hgher LTV ratos than antcpated. To counter ths tendency - whether t s assocated wth market bubbles or smply fundamentally supported prce fluctuatons - t s necessary to contnually montor real estate markets, n partcular to challenge weakenng of underwrtng standards when short-term asset prces are rsng. Ths task requres the development of relable real estate prce ndces. BIS Papers No

14 There s a wde varety of emprcal methods and data sources that could be used to construct real estate prce ndces; as we pont out n ths paper, however, not all can be expected to support the goal of fnancal stablty. One straghtforward method, for example, smply reports the average or medan prce of houses transactng durng each tme perod. Ths method, however, fals to control at all for qualty changes or for changes n the mx of transactng propertes; thus t presents a pcture of asset prce movements that s both based upwards (because qualty ncreases over tme) and unrelable (because the mx of transactng propertes may change durng dfferent parts of the market cycle). A second straghtforward method, reportng the prce of a representatve property, s not well suted for measurng resdental property asset value, snce such propertes transact nfrequently. The hedonc-prce method offers a way of avodng the qualty change, comparablty and narrowness problems assocated wth the frst two methods; unfortunately, the data requred to estmate a hedoncprce model make ths method relatvely expensve to mplement. Because of ths, perhaps the most relable prce ndex method n wde use n the Unted States, for the naton as a whole as well as for the states, s the repeat-sales method, whch requres only two peces of data (transacton prce and date) along wth the troublesome assumpton that no relevant characterstcs of the property changed between transactons. Hybrd models offer the potental to mprove on repeat-sales methods where addtonal data are avalable, but have not yet been shown to be apprecably superor to repeat-sales methods. Several data sources could be used to estmate real estate prce ndces, but many of these are unsutable for the purposes of montorng asset markets and promotng fnancal stablty. Owner assessments of property value, property lstng prces, and property tax apprasals all suffer from severe problems of bas and unrelablty. The best source of data s records of property transacton prces and dates. In the Unted States, these records are commonly collected and made publc n local real property tax assessment systems, many of whch also contan records of hedonc property characterstcs, thus offerng the potental of hedonc-based resdental prce ndces for local areas. References Baley, Martn, Rchard Muth and Hugh Nourse (1963): A regresson ndex method for real estate prce ndex constructon, Journal of the Amercan Statstcal Assocaton, vol 58, pp , December. Case, Bradford, Henry Pollakowsk and Susan Wachter (1991): On choosng among house prce ndex methodologes, Amercan Real Estate and Urban Economcs Assocaton Journal, vol 19.3, pp , Fall. Case, Bradford and John M Qugley (1991): The dynamcs of real estate prces, Revew of Economcs and Statstcs, 73(1), pp Case, Karl and Robert Shller (1989): The effcency of the market for sngle-famly homes, Amercan Economc Revew, vol 79, ssue 1, pp (1990): Forecastng prce and excess returns n the housng market, Amercan Real Estate and Urban Economcs Assocaton Journal, 18, pp Clapp, John and Carmelo Gacotto (1992): Estmatng prce ndces for resdental property: a comparson of repeat sales and assessed values methods, Journal of the Amercan Statstcal Assocaton, 87(418), pp 300-6, June. Fsher, Jeffrey, Dean Gatzlaff, Davd Geltner and Donald Haurn (2003): Controllng for the mpact of varable lqudty n commercal real estate prce ndces, Real Estate Economcs, 31(2), pp Guttentag, Jack and Rchard Herrng (1986): Dsaster myopa n nternatonal bankng, Prnceton Unversty Essays n Internatonal Fnance, no 164, September. Halvorsen, Robert and Henry O Pollakowsk (1981): Choce of functonal form for hedonc prce equatons, Journal of Urban Economcs, 10, pp Herrng, Rchard and Susan Wachter (1999): Real estate booms and bankng busts - an nternatonal perspectve, Group of Thrty, Occasonal Papers BIS Papers No 21

15 (2002): Real estate bubbles, asset prce bubbles: mplcatons for monetary, regulatory, and nternatonal polces, George Kaufman (ed), MIT Press. Kel, Katherne and Jeffery Zabel (1999): The accuracy of owner-provded house values: the Amercan housng survey, Real Estate Economcs, 27(2), pp Malpezz, Stephen, Larry Ozanne and Thomas Thbodeau (1980): Characterstc prces of housng n 59 SMSAs, Washngton DC, Urban Insttute Press. Meese, Rchard and Nancy Wallace (1991): Nonparametrc estmaton of dynamc hedonc prce models and the constructon of resdental housng prce ndces, AREUEA Journal, 19(3), pp Mera, Koch and Bertrand Renaud (2000): Asa s fnancal crss and the role of real estate, M E Sharpe, Armonk. Palmqust, Raymond (1982): Measurng envronmental effects on property values wthout hedonc regressons, Journal of Urban Economcs, 11, pp Pavlov, Andre and Susan Wachter (2003): The anatomy of non-recourse lendng, Wharton Real Estate Center Workng Paper, July. (2004): Robbng the bank: non-recourse lendng and asset prces, Journal of Real Estate Fnance and Economcs, February. Shller, Robert (1993): Measurng asset values for cash settlement n dervatve markets: hedonc repeated measures ndces and perpetual futures, Journal of Fnance, 68(3), pp , July. Thbodeau, Thomas (1992): Resdental real estate prces: , Blackstone, Mt Pleasant, Mch. (1995): House prce ndces from the MSA Amercan Housng Surveys, Journal of Housng Research, 6(3), pp Thompson, Mchelle, Kevn Gllen and Susan Wachter (2003): GIS for real estate valuaton, Wharton Real Estate Center Workng Paper. Tversky, Amos and Danel Kahneman (1982): Avalablty: a heurstc for judgng frequency and probablty, n D Kahneman, P Slovc and A Tversky (eds), Judgement under uncertanty: heurstcs and bases, pp BIS Papers No