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The Quarterly Review of Economics and Finance 51 (2011) 283–291 Contents lists available at ScienceDirect The Quarterly Review of Economics and Finance jo u rn al hom epage: www.elsevier.com/locate/qref Housing price dynamics and convergence in high-tech metropolitan economies Wensheng Kang Department of Economics, Kent State University, 330 University Drive NE, New Philadelphia, OH 44663, United States a r t i c l e i n f o Article history: Received 23 June 2010 Received in revised form 22 February 2011 Accepted 8 May 2011 Available online 26 May 2011 JEL classification: R31 C11 E44 Keywords: Housing prices Spatial diffusion Industrial transmission Convergence GMM Bayesian estimation a b s t r a c t This paper estimates the joint effects of spatial diffusion and high-tech industry transmission on hous- ing prices. I find these effects are significant but generate different short-run dynamics and long-run convergence of housing prices. The spatial diffusion effect is instantaneous but short-lived, whereas the high-tech industry effect is persistent. The dynamics conclusion is supported by estimates of a dynamic panel model using data of 42 high-tech metropolitan areas. A further convergence test shows that it is the high-tech industrial transmission mechanism, not the spatial diffusion, to drive the housing price convergence. © 2011 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved. 1. Introduction In analysis of external determinants of housing prices of a metropolitan statistical area (MSA), spatial modelers often find a ‘ripple effect’ a housing price shock in one area spills over to neighboring regions, with the effect decaying as distance increases (see Meen, 2001; Pollakowski and Ray, 1997). In addition to the spa- tial effect, a number of researchers (see Malpezzi, 2002; Malpezzi, Seah, & Shilling (2004); Nelson, 2001) emphasize the role of high- tech industry in regional housing markets. While Malpezzi (2002) shows that the high-tech economy is a substantial driver of 2000 housing prices, the author suggests that the understanding of such a dynamics relationship is highly desirable. Because ‘tech-poles’ are unevenly distributed geographically, the spatial and industrial effects are likely to be convoluted for some metropolitan areas. The two shocks are likely to drive the housing price convergence in a different dynamic pattern. This paper estimates the effects of spatial shocks and high- tech industry shocks on metropolitan housing prices jointly. I find that these effects show quite different dynamics. A neighboring price innovation diffuses immediately but ends quickly, whereas Tel.: +1 330 308 7414; fax: +1 330 339 3321. E-mail address: [email protected] a high-tech industry shock impacts housing prices persistently. More importantly, a convergence test shows that it is the high- tech industrial transmission mechanism, not the neighboring price diffusion, to enhance the housing market growth and drive the housing price convergence. The research motivation is the current housing market concern that centers around the nature of dramatic boom–bust cycles of housing prices. Some researchers attribute the sustained rise in housing prices beginning in the early to mid-1990s to the devel- opment of high-tech industries, and the subsequent decline in housing prices beginning in the early 2000s to the crash of dot-com bubbles. Further research shows that it is the advances in high-tech industries nationwide that shaped the regional economy and made the geographic distance less important. For example, the positive linkage between the economic outcome and new economy devel- opment is documented in Gorden (2000) and Litan (2001) among others. This paper builds on the previous study to show how the dynamic effects of spatial diffusion and high-tech industrial trans- mission drive the housing price up-and-down in the short run. The second motivation is seeking the price convergence mecha- nism for distant areas via the industrial transmission channel in the MSA level in the long run. First, the spatial diffusion, a ripple effect decaying as distance increases, did not explain the common boom and bust of housing prices for non-neighboring areas. One reason is the difficulty in estimating the spatial effects that the number of 1062-9769/$ see front matter © 2011 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.qref.2011.05.001

Housing price dynamics and convergence in high-tech metropolitan economies

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Page 1: Housing price dynamics and convergence in high-tech metropolitan economies

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The Quarterly Review of Economics and Finance 51 (2011) 283– 291

Contents lists available at ScienceDirect

The Quarterly Review of Economics and Finance

jo u rn al hom epage: www.elsev ier .com/ locate /qre f

ousing price dynamics and convergence in high-tech metropolitan economies

ensheng Kang ∗

epartment of Economics, Kent State University, 330 University Drive NE, New Philadelphia, OH 44663, United States

r t i c l e i n f o

rticle history:eceived 23 June 2010eceived in revised form 22 February 2011ccepted 8 May 2011vailable online 26 May 2011

EL classification:311144

a b s t r a c t

This paper estimates the joint effects of spatial diffusion and high-tech industry transmission on hous-ing prices. I find these effects are significant but generate different short-run dynamics and long-runconvergence of housing prices. The spatial diffusion effect is instantaneous but short-lived, whereas thehigh-tech industry effect is persistent. The dynamics conclusion is supported by estimates of a dynamicpanel model using data of 42 high-tech metropolitan areas. A further convergence test shows that it isthe high-tech industrial transmission mechanism, not the spatial diffusion, to drive the housing priceconvergence.

© 2011 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.

eywords:ousing pricespatial diffusionndustrial transmissiononvergenceMM

aMtdh

thhohbitlood

ayesian estimation

. Introduction

In analysis of external determinants of housing prices of aetropolitan statistical area (MSA), spatial modelers often find a

ripple effect’ – a housing price shock in one area spills over toeighboring regions, with the effect decaying as distance increasessee Meen, 2001; Pollakowski and Ray, 1997). In addition to the spa-ial effect, a number of researchers (see Malpezzi, 2002; Malpezzi,eah, & Shilling (2004); Nelson, 2001) emphasize the role of high-ech industry in regional housing markets. While Malpezzi (2002)hows that the high-tech economy is a substantial driver of 2000ousing prices, the author suggests that the understanding of such

dynamics relationship is highly desirable. Because ‘tech-poles’re unevenly distributed geographically, the spatial and industrialffects are likely to be convoluted for some metropolitan areas. Thewo shocks are likely to drive the housing price convergence in aifferent dynamic pattern.

This paper estimates the effects of spatial shocks and high-

ech industry shocks on metropolitan housing prices jointly. I findhat these effects show quite different dynamics. A neighboringrice innovation diffuses immediately but ends quickly, whereas

∗ Tel.: +1 330 308 7414; fax: +1 330 339 3321.E-mail address: [email protected]

m

nMdai

062-9769/$ – see front matter © 2011 The Board of Trustees of the University of Illinoisoi:10.1016/j.qref.2011.05.001

high-tech industry shock impacts housing prices persistently.ore importantly, a convergence test shows that it is the high-

ech industrial transmission mechanism, not the neighboring priceiffusion, to enhance the housing market growth and drive theousing price convergence.

The research motivation is the current housing market concernhat centers around the nature of dramatic boom–bust cycles ofousing prices. Some researchers attribute the sustained rise inousing prices beginning in the early to mid-1990s to the devel-pment of high-tech industries, and the subsequent decline inousing prices beginning in the early 2000s to the crash of dot-comubbles. Further research shows that it is the advances in high-tech

ndustries nationwide that shaped the regional economy and madehe geographic distance less important. For example, the positiveinkage between the economic outcome and new economy devel-pment is documented in Gorden (2000) and Litan (2001) amongthers. This paper builds on the previous study to show how theynamic effects of spatial diffusion and high-tech industrial trans-ission drive the housing price up-and-down in the short run.The second motivation is seeking the price convergence mecha-

ism for distant areas via the industrial transmission channel in the

SA level in the long run. First, the spatial diffusion, a ripple effect

ecaying as distance increases, did not explain the common boomnd bust of housing prices for non-neighboring areas. One reasons the difficulty in estimating the spatial effects that the number of

. Published by Elsevier B.V. All rights reserved.

Page 2: Housing price dynamics and convergence in high-tech metropolitan economies

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84 W. Kang / The Quarterly Review of E

bservations on each MSA is small relative to the number of MSAsnd the number of regressors. Several authors limit the number ofarameters by focusing on the price dynamics in a small numberf cities.1 This paper uses recently developed Bayesian estimationnd dynamic panel model to refine the work. Second, previoustudies such as Del Negroa and Otrok (2007) and Clark and Coggin2009) find the convergence of housing prices but did not explorehe driving forces behind the convergence. This paper analyzes thendustrial transmission and the spatial diffusion effects on housingrices to show that it is the dynamic effect of high-tech industrialransmission, not the effect of price spatial diffusion, to drive theousing price convergence in the long run.

The third motivation is the exploration of the gain of housingortfolio efficiency obtainable by introducing metropolitan high-ech industrial activities. Based upon the diffusion theory, Quigleynd Van Order (1991) indicate that it benefits housing investorso hedge the housing risks by examining the spatial correlationf metropolitan housing returns. Herdershott, Herdershott, andhilling (2010) suggest that investors substantially overestimatedhe benefits of geographic diversification is one important reasono cause the mortgage finance bubble over recent years. Mueller1993) and Kang (2009) suggest that economic diversificationtrategy is superior to geography based diversification strategy, ifnvestors classify the market in terms of industrial intensities. Thisaper proposes a novel approach to link the housing market activ-

ties and the high-tech industrial dynamics. The linkage implieshat the gain of housing portfolio efficiency is obtainable through

mixture portfolio by combining geographic characteristics andigh-tech industry activities across metropolitan areas.

In this paper I jointly estimate spatial diffusion effects andigh-tech industry effects on housing prices across 42 metropoli-an areas. For each MSA, the measure of spatial influence fromther MSAs is the average of their real housing returns weightedy the distance to that MSA under consideration. To proxy theigh-tech exposure in each MSA, this paper identifies high-techompanies traded on the NASDAQ-technology sector (includingomputer, telecommunication, and biotechnology) with headquar-ers located in the MSA. Then, to measure the high-tech industrialctivities, I construct a metropolitan NASDAQ index (MNI) as thealue-weighted average real return of all high-tech company stocksggregated within the MSA. This proxy is based on the notionhat the stock price reflects investor’s expectation of the high-techndustry and contains useful information for home buyers in theuture. In addition, the MNI also provides a forward-looking indi-ator that is implicitly weighted in accordance with the impactf different sources of high-tech industry fluctuation on the MSAousing prices.

The empirical analysis consists of three steps. First, the papereports some stylized facts in the 42 MSA data from 1981Q1 to007Q4. The study shows that the contemporaneous correlationcross housing price gains are much stronger among ‘tech-poles’han non-‘tech-poles’, which suggests that there may be a strongigh-tech industry effect on housing prices. Second, the work

ointly estimates the spatial diffusion and high-tech industry effectsn all MSAs in a dynamic panel model. I find that these effects arearge in magnitude but different in dynamics. The effect of spatial

1 Cromwell (1992) uses a Vector Auto-Regressive (VAR) model to explore thepatial diffusion process between California state and West regional areas. Clapp,olde, and Tirtiroglu (1995) use the method of moments to estimate spatial diffusionffects of housing prices in satellite cities around San Francisco and Connecticutetropolitan areas. Case and Mayer (1996) find a significant relationship between

ousing values and local manufacturing employment activities within the Bostonetropolitan area.

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ics and Finance 51 (2011) 283– 291

iffusion is short-lived but the high-tech industry effect is per-istent. Third, I conduct a convergence test to show that it is theigh-tech industrial transmission mechanism to drive the housingrice convergence in the long run.

The number of parameters estimated is quite large in the abovexercises. The unrestricted covariance matrix of housing prices of2 MSAs has 903 parameters. The number of price observations onach MSA is only 108. Without restricting the model, the estimatesre likely to be imprecise. Instead of imposing hard constraints,

adopt the Bayesian model selection approach (Smith & Kohn,002) for the covariance matrix. This method searches through theodel space and jointly simulates the probability that a particular

estricted model is correct and the distribution for the parametersonditioning on the restricted model. In estimating the dynamicanel model, I use Generalized Method of Moments (GMM) insteadf least squares. It does so to guard against the possibility that theroxy of the industry-effect contains unmeasured errors and/or

s endogenous (e.g., when the NASDAQ stock and housing pricesre both correlated with a unspecified common factor). The GMMstimate turns out to be better than the least squares estimate.

The rest of the paper is organized as follows. Section 2 describeshe data sources, industrial and spatial variable specification andhe preliminary analysis. Section 3 constructs a dynamic panel

odel of housing prices and reports the GMM estimates. Section conducts tests of the housing-price convergence based on theeographic and industrial classification in the long-run. Section 5ummarizes the main results and discusses the policy implications.he Appendix 5 provides a reanalysis of the data with a structuralreak at 1995Q1. This reanalysis basically confirms the originalesults.

. Data, variable specification and preliminary analysis

.1. Data sources

This paper uses several sources of data: the metropolitan statis-ics area (MSA) housing price indices by Fannie Mae and Freddie

ac, the mortgage rates and NASDAQ stock returns from the Cen-er for Research in Security Prices (CRSP), and other local economicisaggregate data from U.S. Bureau of Census (USBC). The hous-

ng price growth rates, stock returns, income per capital, mortgageates, and construction costs are multiplied by 100 and divided byPI (all items less shelter) based on January 1984, to adjust the

nflation effect.There are two popular datasets for metropolitan housing prices,

edian Home Prices (MHP) produced by National Association ofealtors (NAR), and Housing Price Indexes (HPI) produced by Fred-ie Mac and Fannie Mae and their safety regulator of the Office ofederal Housing Enterprize Oversight (OFHEO). Both datasets areuarterly series reflecting the movement of single-family homerices for metropolitan statistical areas which include the cen-ral cities and their surrounding suburb areas defined by the U.S.ffice of Management and Budget. While the MHP reports mainly

he sales median-prices of existing single-family homes, the HPIeports the combined mortgage pool purchased by Fannie Mae andreddie Mac, including both the appraised value for refinances andales prices for market transactions. The benefits to use HPI arewo fold. First, the HPI is constructed by the weighted repeat-sales

ethod described in Case and Shiller (1989), which keeps the hous-ng price quality constant of same properties over time. Second, the

PI covers a large number of MSAs over a long period of time.

This paper uses the HPI across 42 metropolitan areas from therst quarter of 1981 to the fourth quarter of 2007. Table 1 listshe 42 MSAs used for the analysis in this paper. The work focuses

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W. Kang / The Quarterly Review of Economics and Finance 51 (2011) 283– 291 285

Table 1Partial correlation matrix for 42 MSA housing prices.

Notes: (1) Entries on the diagonal are variances, and entries on the off-diagonal are partial correlations. Only significant entries with estimated posterior probability of beingnon-zero >0.35 are reported. Others are omitted. (2) In the column Reg., W denotes the MSA in the West, E the Northeast, S the South, and M the Midwest. (3) The columnDist. measures the distance between each MSA and San Jose, and the meaurement unit is 10-miles. (4) In the column Grp., H denotes the high high-tech MSA, M the mid-levelM h-PoleN ong, C

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SA, and L the low-level MSA. (5) The left column of Tech-Rank is the rank of “TecASDAQ high-tech sector. The source of “Tech-Pole” ranking comes from DeVol, W

n the study between 1981 and 2007 as most recent studies did,ecause this period captures a full high-tech cycle from the growth

n 1980s, the expansion in 1990s, the recession due to the crashf dot-com bubbles in the early 2000s, to the recovery from 2003o 2007. I examine the 42 MSAs because the fluctuation of hous-ng prices across these areas is heavily studied in recent housingapers. For instance, Nelson (2001) lists 45 high-tech metropoli-an areas and classifies the 45 areas into 3 categories in terms of

high-tech criteria. It motivates me to choose from the 45 areasn order to examine the classification results that is based on theigh-tech industrial intensities.2

.2. Industrial and spatial variable specification

To quantify the driving forces of high-tech industries withinach metropolitan area, I sort out the major high-tech companiesocated in each area by either the headquarters or the main business

nd traded regularly in the NASDAQ-technology sector. This studyhen creates the MNI to measure the high-tech industrial activi-ies by the aggregation of local high-tech company stock returns

2 Three MSAs, Anaheim-Santa Ana, CA, Hartford, CT, and Providence, RI, arexcluded from the Nelson’s (2001) list, since they have missing data at the beginningf the time-series in 1980s. a

” and the right column is the number of high-tech companies regularly traded onatapano, and Robitshek (1999).

ver time. This paper does so to show a simple categorization thatresents the ranking consistency with Nelson’s (2001) classifica-ion. Specifically, a high high-tech metropolitan area is defineds an area containing more than 5 companies traded regularlyn the NASDAQ-technology sector, with more than 30-thousandmployees in the computer, electronic product, and biotechnol-gy manufacturing sectors on average over time. Correspondingly,he low-tech area has less than 2 companies and 10-thousandigh-tech workers. The middle-level areas are between. Then, theNI is constructed as the three-month value-weighted average

eal returns of the major high-tech company stocks within eachetropolitan area given by3

it =

ki∑

i=1

MVi,t−1rit

k,

i∑

i=1

MVi,t−1

3 The result is very similar if the MNI is constructed as the three-month simpleverage returns of the major high-tech company stocks within each MSA.

Page 4: Housing price dynamics and convergence in high-tech metropolitan economies

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86 W. Kang / The Quarterly Review of E

here the Iit denotes the MNI in area i at time t in which ri,t is a 10%tock return in area i at time t.4 The ki is the number of selectedigh-tech companies in an area i, and MVi,t−1 denotes the stockarket value in an MSA.The daily or monthly stock-returns-based measure of industry

ctivity is attractive because the data are reported at a sufficientlyigh frequency to use on a quarterly basis. It also provides a

orward-looking indicator that is implicitly weighted in accordanceith the impact of different sources (e.g., high-tech trade, migra-

ion, employment, taxation and regulation) of high-tech industryuctuation on the MSA housing prices in the dynamics analysis. Inddition, the MSA high-tech companies traded in NASDAQ repre-ent the benchmark of the MSA high-tech industrial activity due toheir dominant capitalization in the area. The MNI proxy is expectedo reflect investor’s expectation of the future of the high-tech indus-ry and contain useful information for home buyers in this area.

Table 1 reports the comparison of the classification with alter-ative ‘tech-poles’ specification provided by the Milken Institution.he term ‘tech-poles’ defined by the Milken Institution is a compos-te measure that ranks the product of the percentage of high-techutput to total output within a metropolitan area and the ratio ofhe high-tech output of the metropolitan area to that of the nationDeVol et al., 1999). The ‘tech-pole’ ranking index is a baseline tother measures of ranking the high-tech metropolitan areas. Fornstance, Nelson (2001) identifies high-tech MSAs by comparinghe ‘tech-pole’ ranking index with five other measures includinghe high-tech employment. The left column of Tech-Rank in Table 1s the rank of ‘tech-poles’ and the right column of Tech-Rank ishe number of high-tech companies regularly traded on NASDAQigh-tech sector in each MSA. The ‘tech-pole’ ranking index showsubstantial overlap with the stock-based high-tech classification.here are 13 high high-tech MSAs that fell in the ‘tech-pole’ rank-ng index 1–15, 11 middle high-tech MSAs in the ranking index5–30, and 10 low high-tech MSAs in the index 30–50. The stock-ased high-tech classification result is also consistent with otherigh-tech employment measures in Nelson (2001), such as Heck-rs’ percentage of high-tech workers and location quotient in anrea. Several outliers are presented showing the difficulty of mea-uring high-tech economy as Nelson (2001) discussed. To guardgainst the possibility that the proxy of the industry effect containsther unmeasured errors, I use both linear and nonlinear instru-ent variables for MNI to obtain consistent estimators in Section

.As most spatial models, the spatial factor is constructed as an

nverse distance weighted function of neighboring housing pricesn the following

it =N−1∑

j=1

1dij

pjt,

here the Sit denotes the spatial factor in area i at time t in which pjts the growth rate of real housing prices in the other area j at time t.ij denotes the physical distance between two metropolitan areas

and j, and N = 42 denotes the number of areas in the estimation.very 10-miles is a measurement unit of distance (e.g., 340 milesean dij = 34). In the column Reg. of Table 1, W denotes the MSA

n the West, E the Northeast, S the South, and M the Midwest. Theolumn Dist. of Table 1 shows the example of the spatial distancef 10 miles between a MSA and San Jose.

4 The scale is based on Case, Quigley, and Shiller (2005) who provide the evidencehat a 10% increase in housing wealth and that in stock market wealth have abouthe same effect on consumption.

Miacrd

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ics and Finance 51 (2011) 283– 291

.3. Contemporaneous correlation of housing prices

Table 1 reports the estimate of a restricted variance-correlationatrix for the growth rates of 42 MSA real housing prices using

mith and Kohn’s (2002) approach. As noted in the introduction, annconstrained 42 by 42 covariance matrix consists of (42 × 43)/2arameters. The precision of the estimates is likely to be adverselyffected by the limited number of observations. Smith and Kohn’s2002) parsimonious estimator has proved efficient to compute theovariance matrix of high-dimensional panel data. They introduce aierarchical binary 0–1 indicator to eliminate the insignificant ele-ents of the partial correlation matrix. These indicators are treated

s unknown parameters to be estimated jointly with the magnitudef the parameters that are with indicator 1. The estimation is viaayesian updating starting at a prior of even odds for non-diagonallements to be 0 or 1.

The lower-triangular elements of Table 1 are partial correla-ions, and the diagonal elements are variances. The blanks meanhe probability of the element is nonzero but below 0.35. The par-ial correlation pattern demonstrates that the housing prices acrosshe highest high-tech metropolitan areas are highly correlated,hereas the housing prices across other areas are either uncorre-

ated or negatively correlated. I classify the 42 MSAs by high-techntensities into the high, middle, and low groups (with 14 MSAsn each group). The table shows that the non-zero contemporane-us correlations of housing prices are stronger for MSAs with theighest high-tech intensity. 48 out of 91 correlation parameters

or MSAs with high high-tech intensity are positive and statisti-ally significant (i.e., those with indicator 1), while 30 out of 91 inSAs of middle high-tech intensity and 23 out of 91 in MSAs of low

igh-tech intensity are significant.The stronger correlation of housing prices across MSAs with

igh high-tech intensity is consistent with the notion that therere common factors that drive future prospects of high-tech firmsnd housing prices in MSAs with a strong presence of high-techompanies. Malpezzi (2002) shows that the collapse of high-techtocks was a substantial driver to the downturn of housing mar-ets across high-tech metropolitan areas in the early 2000s. Thetrong contemporaneous correlations of the housing prices acrossigh-tech MSAs are in agreement with that analysis.

In the following, the analysis moves beyond the contempora-eous relationship of housing prices and examines the dynamicattern of the housing prices in a dynamic panel model acrossSAs.

. Dynamics analysis

Many housing price models are behaviorial models that eitherpecify supply-demand and market equilibrium settings or focusn demand for housing services built on utility maximization. Forhe supply-demand model, Ozanne and Thibodeau (1983), Hendry1984), Topel and Rosen (1988) among others, specify the construc-ion cost and existing housing stock as the primary supply-sideactors, and the population density, personal income per capita, and

ortgage rate as the primary demand-side factors. To build mod-ls that focus on housing demand, Quigley and Van Order (1991),een (2001), among others, consider the problem of maximiz-

ng a lifetime utility function that includes housing services and composite consumption good subject to the budget and creditonstraints. These theoretical structures imply empirically a single

educed-form regression of housing prices on a set of supply andemand factors.

This paper builds on all of these predecessors by considering dynamic panel model in which housing price regressed on the

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conomics and Finance 51 (2011) 283– 291 287

sf

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Table 2Econometric estimate of dynamic panel model (1)

Dependent variable: pit Lags GMM PCSEE

Constant −0.250 *** −0.232 ***

(2.964) (2.747)

Housing price growth rate (pit) −1 0.101 *** 0.105 ***

(3.313) (3.403)−2 0.195 *** 0.197 ***

(7.408) (7.418)−3 0.235 *** 0.234 ***

(8.397) (8.321)−4 0.079 *** 0.078 ***

(3.044) (2.974)

Spatial factor (Sit) −1 0.273 *** 0.272 ***

(3.950) (3.897)−2 −0.096 −0.099

(1.382) (1.401)

High-tech industrial factor (Iit) −1 0.147 ** 0.050(2.022) (1.182)

−2 0.116 * 0.046(1.696) (1.071)

Income per capita (mit) 0.450 *** 0.454 ***

(6.127) (6.156)Population density (nit) 0.318 *** 0.324 ***

(5.473) (5.694)Mortgage rate (rt) −0.044 *** −0.043 ***

(3.434) (3.295)Construction cost (ct) 0.018 0.020

(1.338) (1.456)

Notes: The estimates are obtained by the GMM and PCSEE methods which produceheteroscedasticity robust standard errors. The GMM estimation uses two sets ofinstrument variables for the high industrial factor (i.e., MNI) to obtain consistentestimators. The first set of instrument variables is the third lag to forth lag of realreturns of NASDAQ index. The second set of instrument variables is non-linear toreflect the high-tech booms and busts, that is 1 if the MNI > 0, and −1 otherwise. ThePCSEE is an OLS estimate with heteroscedasticity robust standard errors. Variablesfor housing price growth rates, stock returns, income per capital, mortgage rate, andconstruction cost are adjusted by CPI (all items less shelter) based on January 1984.Values in the parenthesis are absolute values of t-statistics.

* Significant level at 10%.**

tt

3

osrTlhts

itaie

W. Kang / The Quarterly Review of E

patial-industrial effects and a vector of housing supply-demandactors:

it =Lp∑

l=1

�lpi,t−l +Ls∑

l=1

�lSi,t−l +LI∑

l=1

�lIi,t−l + ˘xit + Ai + Bt + �it,

(1)

here

i,t−l =N−1∑

j=1

1dij

pj,t−l and Ii,t−l =

ki∑

i=1

MVi,t−l−1rit−l

ki∑

i=1

MVi,t−l−1

.

ere pit is the growth rate of real housing prices in area i at time t.it−l denotes the lth lag spatial factor in area i at time t. N = 42 is theumber of areas in the regression. Iit−l represents the lth lag high-ech industrial factor in area i at time t (i.e., the MNI as describedn Section 2). The parameters � and � are coefficients of spatial andndustry factors. � is the coefficient matrix for control variablesit = (ct, mit, nit, rt), containing the growth rates of primary supply-emand factors in the housing market: ct is the real constructionost at time t; rt is the real mortgage rate at time t; mit is the realersonal income per capita in area i at time t; nit is the populationensity in area i at time t. Ai and Bt are unobserved area-specificnd time-specific effects. The �it is the serially uncorrelated errorerm since the autocorrelation usually makes the serial correlationisappear (e.g., Beck & Katz, 1995; Greene, 2005).

.1. GMM estimation

Before conducting the empirical analysis, I discuss two tech-ical issues. First, Table 1 shows a strong contemporaneousorrelation of housing prices across high-tech MSAs. The workllows for cross-sectional contemporaneous correlation and het-roscedasticity with the covariance of stacked up error terms �it as

= �N×N ⊗ IT. The elements of � will be estimated from the OLSstimate residuals, �̂i,j = (1/T)

∑Tt=1eite

′jt

.Second, the measure of a metropolitan high-tech industry activ-

ties, MNI, may contain unmeasured errors and be endogenous. Forxample, the subsidiaries of large high-tech companies may scat-er over other metropolitan areas, so the metropolitan NASDAQggregate may capture only a small portion of metropolitan high-ech industrial outcomes. It is also possible that both housing pricesnd stock prices respond to exogenous shocks. In this case the MNIs endogenous. The presence of the measurement error or endoge-ous regressors may lead the simple dynamic OLS estimation to be

nconsistent.To guard against the possibility that the proxy of the industry

ffect contains unmeasured errors and/or is endogenous, I conductMM estimation by using two sets of instrument variables for theNI to obtain consistent estimators. First, I use lags of NASDAQarket returns as instruments. Because the maximum lag of theNI is L = 2, the NASDAQ market returns with more than 2 lags

re uncorrelated with the error term �it can serve as instruments.econd, I use a bivariate indicator as a non-linear instrument vari-ble to reflect the tech booms and busts. That is IVt−l = 1 if the MNI

t−l > 0, and IVt−l = − 1 if It−l ≤ 0. Phillips, Park, and Change (2004)nd Chang (2002) among others have shown that the nonlinear

nstrument variables are effective in correcting inconsistency ofstimators of autoregressive time series models.

As a robustness check, I also compare the GMM estimates witheast squares estimates via Panel-Corrected Standard Error Estima-

tmgt

Significant level at 5%.*** Significant level at 1%.

ion (PCSEE) that does not specify the endogenous MNI variables inhe dynamic panel model (see Beck and Katz, 1995; Greene, 2005).

.2. Estimate results

Table 2 reports the estimate results obtained by GMM and PCSEEf the dynamic panel model (1). The optimal lags of housing prices,patial factors and industrial factors are set equal to 4, 2 and 2espectively by using the lag order selection criteria AIC and BIC.he report presents that the GMM estimates are qualitatively simi-ar to the PCSEE least squares estimates. The point estimates of theigh-tech industry effect show that PCSEE is quantitatively worsehan GMM, suggesting that the industry factor contains unmea-ured errors and/or is endogenous.

The autoregressive coefficients of the growth rate of real hous-ng prices are significant and positive. The result is consistent withhe well-known findings by Case and Shiller (1989), Clayton (1998),mong others. The persistent autocorrelation of housing pricesmplies the housing market inefficiency. There are primarily twoxplanations of the inefficiency. First, investors’ irrational expec-ations generate self-fulfilling price dynamics. Second, housing

arket imperfections, such as information imperfections, hetero-eneities, large transaction costs, and differential local taxes, causehe housing-market inefficiency. The magnitude of autoregressive

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ig. 1. The responses of housing prices to the spatial and high-tech industrial innoertical axis represents the response of housing prices, and the horizontal axis is tim

oefficients tends to diminish over time, showing a slow mean-eversion of housing prices in the long run.

All coefficients of control variables have expected signs. Theargest effect on housing prices is real income followed by popu-ation densities. The moderate positive effect of construction costsnd the significant negative effect of mortgage rates are consis-ent with previous housing studies such as Clapp et al. (1995), Casend Mayer (1996), among others. The point estimates of high-techndustrial effects on the housing-price growth rates are 0.147 for lag

and 0.116 for lag 2 and are significant at 5% and 10% significantevels respectively, in the sense that a 10% increase in high-techndustrial stock prices has about an increase of 0.12% in housingrices on average. The coefficients of spatial effects are significantnd positive for lag 1 with a large value 0.273, in the sense that% increase in the 10-mile-away agglomerating housing prices hasbout an increase of 0.273% in the central area’s housing prices.owever, the coefficient of lag 2 is −0.096, showing a quick rever-

ion. The short-lived spatial effect shows that the housing investorsre sensitive to the change in neighboring housing prices. Housingpeculators expecting to reap huge profits in the short run mayxhaust and use-up the neighboring-price information quickly. Inontrast, the significant high-tech industrial effect is long lasting.t reflects that the expectation on the industrial development leadshe demand of home-buying in the future.

.3. Dynamic response analysis

The point estimate of the dynamic panel model provides theork a base to analyze and forecast the spatial and high-tech indus-

plts

s, along with two standard error bands computed by Monte Carlo simulation. The

rial effects on housing prices in the long horizon. I utilize theynamic response functions based on the estimation to simulatehe dynamic multiplier of an one-unit change in spatial or indus-rial factor z on the j-period ahead growth rate of real housing price, ∂pt+j/∂zt. For spatial effect at t = 0: (1) set S0 = 1, and set the valuesf the other independent variables for other dates to 0; (2) calcu-ate p1 = �1, and substitute this value along with other independentariables back into the model; (3) calculate p2 = �1�1 + �1; and (4)ontinue recursively to calculate pj. The recursive process com-utes the responsive effect of a spatial innovation of S0 on pj at time

ahead. The same procedure is applied to compute the responsiveffect of high-tech industrial innovation.

I compute the dynamic response functions to forecast thempact effect of spatial and high-tech industrial factors on housingrices in 12 lags, along with two standard error bands computedy Monte Carlo simulation. As Fig. 1 shows, the initial responsesf housing prices to the spatial factor are significant and posi-ive. The impact effect declines sharply and becomes negative inhe second lag, turns positive with the economic expansion in thehird period, and then tapers off smoothly. In contrast, the indus-rial effect is significant at the first lag, reaches the peak at theecond lag, and then dies off with substantial lags. It confirmshe point estimates that the spatial diffusion effect is instanta-eous but short-lived, while the high-tech industrial effect is quiteersistent. The result provides useful information for investment

ractitioners who are making efforts to refine real estate portfo-

ios under risk-return diversification consideration, in the sensehat constructing a geography-and-industry mixture portfolio mayubstantially reduce the investment risks in the housing market.

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W. Kang / The Quarterly Review of Economics and Finance 51 (2011) 283– 291 289

Table 3Panel unit-root test on housing price convergence.

Convergence test on tech-industry baseline Convergence test on geography baseline

Group Average Minimum Group Average Minimum

All (N = 42) −1.225 −5.043 *** West (N = 11) −0.445 −3.687 ***

High (N = 14) −1.414 * −4.121 *** South (N = 11) −1.085 −3.352 ***

Middle (N = 14) −1.016 −2.263 Northeast (N = 8) −4.192 *** −5.534 ***

Low (N = 14) −0.528 −3.336 *** Midwest (N = 11) −1.087 −2.036

Notes: The real housing price is demeaned in each area at first. We then conduct the panel unit root test on the differentials between the natural logarithm of real housingprices at area i at time t and the natural logarithm of the cross-sectional average on each group. The significance of the test on each group means stationarity that is there isevidence of the convergence of housing prices in that group. The average test refers to the standard normal inference with the critical values that are given by −2.325, −1.645,and −1.282 at 1%, 5% and 10% levels respectively. The minimum test refers to Chang (2002) and Chang and Song (2009) and varies in term of N. For N = 42, the critical valuesare −3.479, −3.016, and −2.791, respectively, at 1%, 5% and 10% levels. For N = 14, the critical values are −3.166, −2.657, and −2.406, respectively, at 1%, 5% and 10% levels.For N = 11, the critical values are −3.089, −2.568, and −2.309, respectively, at 1%, 5% and 10% levels. For N = 6, the critical values are −2.877, −2.319, and −2.036, respectively,at 1%, 5% and 10% levels. **Significance at 5% level.

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. Convergence test

The persistent effect of high-tech industry on housing pricesotivate me to investigate whether the high-tech industry trans-ission leads to housing price convergence across high-techetropolitan areas in the long run. Previous studies such as Delegroa and Otrok (2007) and Clark and Coggin (2009) find the con-ergence of housing prices but did not explore the driving forcesehind the convergence. This paper explores the high-tech indus-rial channel of transmission in which high-tech industrial shocksffect housing prices in the long run.

The following convergence test provides insight on the inter-ction of spatial and high-tech industrial factors in determininghe housing price convergence in the long run. I test the housingrice convergence based on the four-region grouping: West, Mid-est, Northeast, and South groups, and on the high-tech intensity

rouping: high, middle, and low-level groups. Specifically, I let (Pit)e the natural logarithm of real housing prices of area i at time, and (P̄t) be the natural logarithm of the cross-sectional averageor each group market. The test for housing price convergence con-erns whether the differentials in the following form are stationaryn each group market

it = (Pit − P̄t). (2)

f the differential (yit) is stationary then there is strong evidence ofonvergence to the common trend. The test for housing price con-ergence therefore amounts to the test for stationary of the housingrice differentials yit from all areas in each group, with the nullypothesis being that there is a unit-root in yit. This methodology

s used by Chang and Rhee (2005) to test cross country convergence.o test the stationarity of the grouping-benchmark deviation (yit), Ipply Chang and Song (2009) non-linear panel unit root test whichas the advantages of exploiting the time-series properties for aanel with long time periods while allowing for various hetero-eneities and error dependencies in general across sections. Theest methodology is also fitted to the housing prices that are non-inear identified in Kim and Bhattacharya (2007).

The left panel of Table 3 reports the results for metropolitanousing price convergence by high-tech grouping, and the rightanel reports the results by spatial grouping. For metropolitanousing prices as a whole, the average test does not reject theon-stationary null against a fully stationary panel, although the

inimum test rejects the non-stationary null in favor of a mixture

tationary panel at the 10% significant level. The evidence rejectsrice convergence among all MSAs but leaves a possibility of priceonvergence among some MSAs in the housing market. The same

eiir

onclusion can be drawn for the middle and low-level tech areas asell as the West and South regions. For Midwest region, neither the

verage test nor the minimum test reject the non-stationary nullgainst a fully stationary panel. It implies the absence of a com-on trend of housing prices across MSAs in the Midwest region.

he evidence that regional convergence is mixed is consistent withlark and Coggin (2009).

For high high-tech areas, however, both the average and mini-um tests reject the unit root hypothesis at least at 10% significant

evel in favor of either a fully stationary panel or a mixture one withome stationary units. These results show that the housing priceonverges to the mean across high high-tech MSAs. The price con-ergence implies that high-tech industrial shocks emanate fromne high-tech MSA may cause multiplicative effects on housingrices of another distant high-tech MSA. It is well-known that theeal business cycle theory implies that technological innovationffects real business cycle rather than aggregate demand shocks.he study provides the supporting evidence in the housing markethat the high-tech industry transmission, not the spatial diffusion,eads to housing price convergence across high-tech metropolitanreas in the long run. The economic environmental similarity dueo the creation of high-tech industries is capable of absorbing theconomic spill over effect to drive the price convergence of distantetropolitan housing markets. The result provides useful informa-

ion for policy makers in that a nationwide housing policy mightut the housing market convergence at risk due to the asymmetriceactions across MSAs with different high-tech industrial intensi-ies.

. Conclusion remarks

This paper uses a dynamic panel model to examine the spa-ial diffusion and industrial transmission effects on metropolitanousing prices jointly. I conduct GMM estimation of a dynamicanel model by using quarterly housing-price data from 1981 to007 for 42 MSAs. The estimate presents that the diffusion ofhe spatial effect is instantaneous but short-lived, whereas theffect of high-tech industry shock is persistent. I also conducthe convergence test showing that it is the high-tech industrialransmission mechanism, not the spatial diffusion, to enhance theousing market growth and drive the housing price convergence.

t indicates that the expectation on the industrial development

ssentially leads the demand of home-buying in the future. Themportance of high-tech industrial effects suggests that construct-ng a geography-and-industry mixture portfolio may substantiallyeduce the investment risks in the housing market.
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90 W. Kang / The Quarterly Review of E

cknowledgements

The author would like to thank John S. Howe, Douglas J. Miller,

hawn Ni, Jing Wang, the editor Hadi Salehi Esfahani, and threenonymous referees for helpful comments on an earlier draft ofhis article. Any errors or omissions, however, are the author’sesponsibility.

THsm

able A.1obust estimate of dynamic panel model (1).

Dependent variable: pit Lags 1981Q1–1994Q

GMM

Constant −0.401 ***

(3.601)

Housing price growth rate (pit) −1 −0.003

(0.182)−2 0.018

(1.015)

−3 0.026

(1.603)

−4 0.016

(0.912)

Spatial factor (Sit) −1 0.434 ***

(2.938)

−2 −0.012

(0.081)

High-tech industrial factor (Iit) −1 0.273 *

(1.735)−2 0.165

(1.080)

Income per capita (mit) 1.333 ***

(37.226)

Population density (nit) 0.193 ***

(10.838)Mortgage rate (rt) −0.012

(0.862)Construction cost (ct) 0.015

(1.021)

otes: The estimates are obtained by the GMM and PCSEE methods which produce heterosariables for the high-tech industrial factor to obtain consistent estimators. The first set of

ndex. The second set of instrument variables is non-linear to reflect the high-tech boomsith heteroscedasticity robust standard errors. Variables for housing price growth rates, s

y CPI (all items less shelter) based on January 1984. Values in the parenthesis are absolu* Significant level at 10%.

** Significant level at 5%.*** Significant level at 1%.

able A.2obust panel unit-root test on housing price convergence.

Test on tech-industry baseline (1980Q1–1994

Average Minimum

Panel A. Nonlinear IV unit root testsAll (N = 42) −1.046 −3.274 ***

High (N = 14) −1.025 −3.245 ***

Test on tech-industry baseline (1980Q1–1994

IPS MP

Panel B. Other existing unit root testsAll (N = 42) −18.503 *** 124.49High (N = 14) −7.977 *** 44.148

otes: The real housing price is demeaned in each area at first. We then conduct the panrices at area i at time t and the natural logarithm of the cross-sectional avelerage on eachvidence of the convergence of housing prices in that group. The avelerage test refers to t1.645, and −1.282 at 1%, 5% and 10% levels respectively. The minimum test refers to Chaalues are −3.479, −3.016, and −2.791, respectively, at 1%, 5% and 10% levels. For N = 14,

evels. IPS test is by Im, Pesaran, and Shin (2003), and MP test is by Moon and Perron (200* Significance at 10% level.

*** Significance at 1% level.

ics and Finance 51 (2011) 283– 291

ppendix A.

In this section, I provide robust estimates on model (1), in

able A.1, and other convergence tests, in Table A.2, respectively.ousing literature shows home prices have been rising strongly

ince the mid-1990s. The housing price bubble beginning in theid-1990s suggests a structure break would exist at about the

4 1995Q1–2007Q4

PCSEE GMM PCSEE

−0.355 *** −0.476 ** −0.426 *

(3.271) (2.128) (1.879)

−0.002 −0.017 −0.017(0.118) (0.403) (0.396)0.019 0.030 0.031(1.089) (0.698) (0.735)0.028 * 0.037 ** 0.039 *

(1.692) (1.954) (1.701)0.016 0.036 0.037(0.909) (0.795) (0.809)

0.434 *** 0.318 0.295(2.933) (1.110) (1.025)−0.014 0.160 0.173(0.094) (0.560) (0.600)

0.080 −0.214 −0.053(0.873) (0.544) (0.241)0.071 0.827 ** −0.134(0.808) (2.179) 0.606

1.339 *** 1.002 *** 1.002 ***

(37.472) (15.398) 15.3920.193 *** 0.116 *** 0.115 ***

(10.853) (6.920) (6.841)−0.009 −0.076 *** −0.074 ***

(0.652) (3.630) (3.586)0.021 0.013 0.012(1.466) (0.640) (0.550)

cedasticity robust standard errors. The GMM estimation uses two sets of instrumentinstrument variables is the (i.e., MNI) third lag to forth lag of real returns of NASDAQ

and busts, that is 1 if the MNI > 0, and −1 otherwise. The PCSEE is an OLS estimatetock returns, income per capital, mortgage rate, and construction cost are adjustedte values of t-statistics.

Q1) Test on high-tech baseline (1995Q1–2007Q4)

Average Minimum

−1.197 −2.931 *

−3.442 *** −3.958 ***

Q1) Test on high-tech baseline (1995Q1–2007Q4)

IPS MP

4 *** −11.575 *** 61.925 ***

*** −2.064 *** 19.929 ***

el unit root test on the differentials between the natural logarithm of real housing group. The significance of the test on each group means stationarity that is there ishe standard normal inference with the critical velalues that are givelen by −2.325,ng (2002) and Chang and Song (2009) and varies in term of N. For N = 42, the criticalthe critical values are −3.166, −2.657, and −2.406, respectively, at 1%, 5% and 10%4). The IPS and MP tests include trend and 5 lags. **Significance at 5% level.

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eginning of 1995.5 I divide the data into two samples with areak on January, 1995. The parameter estimate in Table A.1 showshat the high-tech industrial effect is more pronounced after 1995,hereas the convergence test in Table A.2 rejects the unit root in

he panel showing that house prices converge to the mean acrossigh high-tech MSAs.

Table A.2 provides other existing unit root tests by Im et al.2003) (IPS later) and Moon and Perron (2004) (MP later). All testseject the unit root hypothesis in favor of housing price conver-ence before and after 1995. The IPS test assumes cross-sectionalndependence and the MP test uses an approximate dynamic lin-ar factor model. If the co-integration across cross-sectional unitsn the long-run and the cross-sectional dependence of innovationsn the short-run exists, the IPS and MP tests suffer from the low dis-riminatory power problem and are invalid (see Chang and Song,009).

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