ISSN (Print) 0473-453X

Discussion Paper No. 1197

ISSN (Online) 2435-0982

Housing and Macroeconomics

Charles Ka Yui Leung

November 2022

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Housing and MacroeconomicsΩ
Charles Ka Yui LeungΞThis version: November 2022
Summary
Until recently, the literature ignored the interactions between housing and macroeconomics.
Thanks to many researchers' contributions, the macro-housing field is in development. This
review complements previous research and highlights a few areas that have made significant
progress lately. They are the rental market and related issues, housing affordability, people's
beliefs and expectations, and the interactions between the aggregate and regional markets.
Theoretical models have become increasingly realistic and hence can be solved only
numerically. While the recent literature has provided essential policy lessons, it has yet
delivered a "paradigm" for future research. There are also open questions that remain to beanswered.

Keywords: aggregate and regional shocks, belief and expectation, rental market, housing
JEL Classification: E10, E30, G50, R20, R30

Acknowledgement: This article is prepared for the Oxford Research Encyclopedia of Economics and Finance.
The author thanks all the co-authors, colleagues, and friends for many stimulating thoughts over the years,
especially Kuang-Liang Chang, Nan-Kuang Chen, Yongheng Deng, Yifan Gong, Eric Hanushek, Fred Kwan,
Steve Malpezzi, Joe Ng, Tim Riddiough, Jim Shilling, Edward Tang, Chung Yi Tse, Ping Wang, Kuzey Yilmaz,
Matthew Yiu, among others. The usual disclaimer applies.
Ξ
Correspondence: Leung, Department of Economics and Finance, City University of Hong Kong, Kowloon
Tong, Hong Kong, kycleung@cityu.edu.hkΩ1. Introduction

This article reviews the literature on housing and macroeconomics. 1 We begin with afew observations. First, while “housing” and “macroeconomics” have been studied for
decades, joint study is a relatively new field (Leung, 2004). For instance, within the JEL
classification system provided by the American Economic Association, category “E” isMacroeconomics and Monetary Economics. 2 It is interesting that while another relatively
new field, “Macro-Based Behavioral Economics” is included as E7, “Macroeconomics andhousing” or “Macroeconomics and real estate” is not found. 3 Similarly, while category “R” isdevoted to Urban, Rural, Regional, Real Estate, and Transportation Economics, “Housing andMacroeconomics” or “Real Estate and Macroeconomics” cannot be found. 4
Such mutual exclusion may stem from the missing housing market in the traditional
macroeconomic model. A typical Macroeconomics textbook often focuses on determining thegeneral price level and inflation, wage and employment, and the gross domestic output(GDP). Housing is never an important topic. 5
Recent research has confirmed that residential properties, commercial real estate, industrial real estate, realestate-backed securities, land, etc., also have deep connections with the macroeconomy. It is beyond the scopeof this article to review the literature. Interested readers should consult Bhattacharya (2022), Fu and Vaird(2022), Hattori and Yoshida (2022), Kahn et al. (2022), Leung and Chen (2006), Ng et al. (2022), Ogawa(2022), Ogawa et al. (1996), Riddiough (2022), Sagi (2021), among others.
2
The JEL classification system can be found here:
https://www.aeaweb.org/econlit/jelCodes.php?view=jel&print. Notice that JEL classification is a complicatedissue in itself. According to Cherrier (2017, p.546), “… When John Pencavel, then editor of the Journal of
Economic Literature (JEL), initiated in 1988 the revision that created the classifications we use today, it took
him two days to work out how to classify microeconomics. But macroeconomics took no less than two years ofcontroversy. Previous revisions had also been very difficult,… The history of the JEL codes is thus essentially astory of how economists have perceived their discipline.”
3
E0 is “General,” E1 is “General Aggregative Models,” E2 is “Consumption, Saving, Production, Investment,
Labor Markets, and Informal Economy,” E3 is “Prices, Business Fluctuations, and Cycles,” E4 is “Money andInterest Rates,” E5 is “Monetary Policy, Central Banking, and the Supply of Money and Credit,” E6 is“Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook.”
4
R0 is “General,” R1 is “General Regional Economics,” R2 is “Household Analysis,” R3 is “Real Estate
Markets, Spatial Production Analysis, and Firm Location,” R4 is “Transportation Economics,” R5 is “Regional
Government Analysis.”
5
Housing is often mentioned on a few occasions. In the study of national income accounting, students ofmacroeconomics are reminded that the value of primary housing market transactions, but not the counterpart ofthe secondary housing market, is included in the GDP to avoid double counting. Empirically, the secondary
housing market is much larger than its primary counterpart in the United States and many economies. Second,1Fortunately, the undergraduate textbook is only sometimes a good indicator of theresearch frontier. Many have contributed to the overlapping research interest in housing andmacroeconomics. 6 For instance, following the spirit of Cooley and Prescott (1995), Leungand Ng (2019) report the stylized facts of the U.S. housing market variables, including thereal house price, the new house sold, vacancy rate, and real residential investment. They findthat at business cycle frequency (i.e., periodicity of 6 to 32 quarters), the correlations between
the housing market and macroeconomic variables are generally weakened. In contrast, thecorrelations between the housing market and macro-financial variables were strengthened
after the Global Financial Crisis (GFC) in 2008. 7 Ng (2022) examines whether a similar
weakening occurs between housing prices and macroeconomic variables among the OECD
countries. While the correlations between housing prices and GDP have weakened in Austria,
Belgium, Denmark, France, Iceland, Ireland, Israel, South Korea, Netherlands, and Norway,
the correlations between housing prices and unemployment rates have strengthened in
France, Iceland, Italy, and Lithuania. 8 In other words, significant heterogeneity exists among
nearby countries.
Our second observation is that housing and macroeconomic research have dramaticallychanged. Housing research, and urban economics in general, has extended from the intra-city
spatial allocation to become more connected to other fields, such as environmental

housing rent, but not the housing price, is a component of the consumer price index (CPI), although the latter isreported at least as frequently as the former in the media. For instance, see Hubbard and O’Brien (2022).
6
Among others, see Bardhan et al. (2012), Baxter (1996), Ben-Sahfar et al. (2008), Chang (2000), Chen et al.
(2015), Chen and Leung (2008), Chen and Wang (2007), Davis and Heathcote (2005), Edelstein and Kim(2004), Glaeser (2013), Greenwood and Hercowitz (1991), Hendershott et al. (2010), Iacoviello (2005),
Justiniano et al. (2016), Kaplan et al. (2020), Kiyotaki and Moore (1997), Kwan et al. (2015), Leung (1999,
2001, 2003, 2004, 2007, 2014, 2022), Leung et al. (2006), Leung and Quigley (2007), Leung and Tsang (2023),
Malpezzi (2017), Ortalo-Magné and Rady (2006), Tse and Leung (2002), Wang and Xie (2022), etc.
7
The macroeconomic variables include the real GDP, unemployment rate, CPI, non-residential investment,
consumption, and trade surplus. The macro-financial variables include the Federal Fund rate (FFR), term spread,
TED spread, external finance premium, and stock market index (S&P 500).
Leung and Ng (2019) also discuss the literature on bankruptcy and mortgage contracts, search and belief, urban
policy, human capital, etc.
8
See also Green (2022).

economics, health economics, labor economics, finance, etc. It also looks beyond theadvanced economies and contributes to our understanding of emerging markets (Bryan et al.,2019; Case et al., 2005; Deng et al., 2000; Eichholtz et al., 2010; Fu and Viard, 2021; Glaeser
et al., 2004, 2010; 2019; Glaeser and Kahn, 2010; Goldstein and Moses, 1973; Huang et al.,2015; Malpezzi, 1999, 2001, 2018, 2020; McDonald and McMillen, 2010; Quigley and Van
Order, 1995; Tiwari and Rao, 2022; Wu et al., 2012, 2016).
For macroeconomics, changes are more methodological. It switches from an ad hoc
econometric model to a representative agent model with micro-foundation and then to aheterogeneous agents model (Ahn et al., 2018; Azariadis, 2018; Kaplan and Violante, 2018;
Krueger et al., 2016; Lubik and Surico, 2010; Lucas, 1976; Lucas and Sargent, 1978). It alsorelies more on micro-econometric techniques than time series models over time (Glandon etal., 2022). All these changes have important implications for macro-housing research. Several
authors have reviewed the macro-housing literature (Davis and Van Nieuwerburgh, 2014;
Duca et al., 2021; Leung and Ng, 2019; Piazzesi and Schneider, 2016). This chapter
complements the existing works by highlighting more recent developments and leaves an
exhaustive literature review for future research.
1.

Rental Market and related issues
Rental market research may be one of the most active areas in macro-housing, and it
deserves such devotion. According to OECD (2022), the average homeownership rateof the OECD countries, including "own outright" and "owner with mortgage," isabout 70%, suggesting that a significant fraction of the population lives in rental
housing. It is not surprising that there is extensive literature on "tenure choice" (i.e.,
rent or own) in urban economics. 9 The housing affordability literature also starts to

Notice that in labor economics, “job tenure” refers to the duration a worker remains with the same employer,
which is very different from the notion of “tenure” in urban economics. Among others, see Lazear (1991) for
more details.

9pay attention to the rental market. 10 Yet, in classical macro-housing papers (Davis andHeathcote, 2005; Iacoviello, 2005; Kiyotaki and Moore, 1997; Ortalo-Magne andRady, 2006), the rental market is typically assumed away. Some studies consider therental market in a dynamic equilibrium setting (Kan et al., 2004; Leung, 2007, 2014).
However, the rental option is not exercised in equilibrium due to the representative
agent assumption. Some search-theoretic works model both the for-sale and rental
markets (Burnside et al., 2016; Leung and Tse, 2017; Ngai and Sheedy, 2020). 11
When households move out of their original houses and search for new ones, they live
in rental housing. For convenience, the rents are assumed to be exogenous orinsensitive to the market tightness of the for-sale market.
There are efforts to endogenize both the rent and price in an equilibrium setting. For
instance, Chambers et al. (2009a, b) build a life-cycle model with endogenous rent
and price. They focus on matching the age-dependent homeownership rate and how
the mortgage contract choice would affect the homeownership decision. The rent-toprice ratio is not a target of their calibration. Halket and Vasudev (2014) build anotherlife-cycle model with endogenous rent and price. They focus on how thehomeownership rate increases and mobility decrease over the life cycle. Their model
matches the renter moving rate, but the equilibrium rent-to-price ratio is not acalibration target. Halket and Pignatti Morano di Custoza (2015) propose acompetitive search model. Their calibration suggests that the rent-to-price ratio is
between 3% to 4% in all submarkets, which seems to be at odds with the microevidence (Colonnello et al., 2021; Waltl, 2018). Yao (2021) extends the framework ofThis paper cannot do justice to reviewing the enormous literature on tenure choice in urban economics. Amongothers, see Dietz and Haurin (2003), Goodman and Kawai (1982), Goodman and Mayer (2018), Haurin et al.
(2002), Kan (2000), MacRae and Struyk (1977), Ortalo-Magné and Rady (2008), Painter et al. (2001), and thereference therein.
10
For instance, see Albouy et al. (2016), Malpezzi (2020, 2023), Yılmaz and Yeşilırmak (2023).
11
See also Genesove and Han (2012), Krainer (2001), among others.

Chambers et al. (2009a, b) and allows developers to choose between building houses
for owners or apartments for renters. She can match the inter-city differences in priceand rent in the United States. Parkhomenko (2020) study a spatial equilibrium modelwith an endogenous level of regulation. Like Ortalo-Magné and Prat (2014), owners
in his model have incentives to choose an excess degree of regulation and enjoy ahigher rent level. Since renters are mobile across cities, land use regulation distorts
rent and labor allocation in equilibrium. However, since homeowners are the majority
in the U.S., deregulation might not lead to significant aggregate welfare gains. 12
Some authors build equilibrium models of the rental market and apply them to local
markets. For instance, Huang et al. (2018) studied a set of 132 popular real estatedevelopment (RED) in Hong Kong. Using the conventional matching estimator, theycalculate the RED-level rent-to-price ratios and find those ratios differ significantly
across RED and systematically vary with the average income level, the educationlevel, and the demographic characteristics of the households in the RED. Han et al.
(2022) study the housing market in Toronto and how the transaction taxes change
people's choice of rental vs. purchase of housing. 13
Greenwald and Guren (2021) stress that the operation of the rental market is crucial to
our understanding of how the credit market would affect the housing market. In an
earlier period (1965~97), the price-rent ratio of the United States varied, but its
movement is uncorrelated to the change in the homeownership rate. Starting in 1997,
the price-rent ratio and homeownership displayed positive co-movement most of thetime. They show that if the rental and owner-occupied markets are perfectly
segmented, in the sense that one cannot convert owner-occupied housing into rental

This result contrasts Herkenhoff et al. (2018), where regulation is exogenous.
Landvoigt et al. (2015) also consider an assignment model and apply it to the housing market in San Diego.
However, the rent-to-price ratio is not their focus.
12
13

housing and vice versa, the price-rent ratio and homeownership rate would be
uncorrelated. On the other extreme, if the markets are frictionless in that one cancostlessly trade between the rental and owner-occupied markets, the price-rent ratio
and homeownership rate would also be uncorrelated. It is only in the intermediate"frictional" case, i.e., when one can convert the rental and owner-occupied housing
units into one another with cost, then the price-rent ratio and homeownership ratewould be positively correlated. Based on these observations, they provide both
reduced form and structural estimation. They find that credit supply can explain
between 35% and 54% of the rise in price-rent ratios over the 2000s housing boom.
Moreover, the house price dynamics generated by the calibrated dynamic equilibrium
model are close to that under perfect segmentation, suggesting significant frictions in
American rental markets.
Conventional theoretical models typically assume that renters pay rent. In practice, it
might be different. Just like homeowners may fail to pay the monthly mortgage
payment to the point that they might default on their loans, renters may fail to pay
rent. 14 According to Collinson et al. (2022, p.1), “More than two million eviction
court cases are filed in the United States each year. These cases predominantly
involve low-income and minority households. About half of proceedings end in acourt order for eviction: a judgment requiring the tenant to vacate the property.”
Moreover, there are significant cross-country differences. Collinson et al. (2022, p.1)
report that the “U.S. is an outlier in the number of eviction cases per renter household,with a rate 1.5 times higher than the next-highest country (Canada),” among OECD
countries. Using quasi-experimental instrumental variables (IV), Collinson et al.

The literature on mortgage default is too large to be reviewed here. Among others, see Deng et al. (2000),
Jones and Sirmans (2015, 2019), Kalikman and Scally (2022), LaCour-Little (2008), Leung and Ng (2019), andthe reference therein.
14

(2022) argue that in Cook County (which includes Chicago) and New York City,
eviction increases homelessness and reduces earnings. Extensive literature shows that
homelessness can cause other social problems. 15
Naturally, one would ask why evictions arise in the first place. And can we or should
we reduce evictions? Some recent research may shed light on these critical questions.
For instance, Corbae et al. (2022) build a directed search model of the rental market
when renters face idiosyncratic labor income shocks. Under some parameterization,
eviction is an equilibrium outcome. Abramson (2021) builds an overlapping
generations model in which households face idiosyncratic labor income shocks, andthe housing supply is endogenous. He then calibrates the model with detailed
microdata from San Diego county in the United States. He then conducts
counterfactual experiments. It is well known that rent control does not necessarily
reduce the equilibrium rent and improve the welfare of renters, as the housing supply
is endogenous in the long run. 16 By the same token, Abramson (2021) finds that the"Right-to-Counsel," which makes it harder for the landlords to evict, would notdecrease the equilibrium eviction rates, as default is an endogenous decision. On thecontrary, rental assistance reduces eviction and homelessness. Even considering thetax burden, rental assistance policy can improve social welfare. In other words, there
may be no free lunch in "solving" the eviction and homelessness problem. In otherwords, there may be no free lunch in “solving” the eviction and homelessness
problem. 17

The literature is too large to be reviewed here. Among others, see An et al. (2022), Clifford et al. (2019),
Cobb-Clark and Zhu (2017), Cohen (2022), Grech and Raeburn (2019), and the reference therein.
16
The literature on rent control is too large to be reviewed here. Among others, see Diamond et al. (2019),
Glaeser and Luttmer (2003), and the reference therein.
17
The United States is not the only country facing the homeless problem. For instance, see O'Flaherty et al.
(2018) for the case of Australia.
15

More generally, governments intervene in the rental market as they intervene in theowner-occupied market. According to OECD (2022), “renting at subsidised rates ismost common in the United Kingdom (20%), France (19%) and Ireland (18%), and isalso common in Iceland, and Finland.” 18 There are two primary forms of subsidy: thegovernment provides rent subsidies to targeted households searching for their desired
units or directly provides public rental units. Some countries provide both forms ofassistance.
As predicted by economic theories, different government subsidies always bring a
crowding-out effect, meaning that the total housing supply would not increase as
much as the government policy would desire because the private sector supply would
reduce. Empirically, crowding out is often less than one-to-one, meaning that forevery housing unit a government policy intends to increase, the total housing supply
increases by less than a unit. 19 Moreover, there are cases where appropriately
designed public housing policies can assist the economic recovery after WWII. 20
On the other hand, government subsidies need to be eventually financed by the tax.
Thus, the question is whether the benefit of such government interventions in therental market outweighs the cost. Moreover, the general equilibrium effect can besignificant. Hence, structural models are needed. For instance, combining theconventional wisdom of Alonso and Tiebout models, Hanushek et al. (2011),
Hanushek and Yilmaz (2007, 2011, 2022) build a series of urban models to match an

OECD (2022) also remarks that “Austria, Denmark and the Netherlands – countries where support for
subsidised rental housing is traditionally sizable -- are not considered here due to data limitations.” Hence, thesubsidized rental housing in those countries could also be considerable.
19
The literature is too large to be reviewed here. Among others, see Chen and Nong (2016), Malpezzi and
Vendell (2002), Murray (1983), Sinai and Waldfogel (2005), and the reference therein.
20
For instance, see Dalmazzo et al. (2022) for the case of Italy, Li and Yu (1990) for Hong Kong, and Phangand Helble (2016) for Singapore.
18

“average” American city and examine different public policy options. 21 Leung et al.
(2012) show that in the Hanushek-Yilmaz framework, housing voucher brings higher
social welfare than public housing. Gong and Leung (2020) calibrate a model thatmatches some stylized facts in the American labor and housing markets. They findthat combining school finance consolidation and public housing at the city's edge
could lead to a Pareto improvement. Yılmaz and Yeşilırmak (2023) extend theHanushek-Yilmaz framework and compare the impacts of housing vouchers andtransportation vouchers in different socio-economic outcomes. 22 Most of these papers
are calibrated to match the U.S. situation. Given that the institutional settings andmarket structure vary significantly across countries, there may not be a "universally
optimal" policy for the rental market. More research efforts would be needed.
2.

Housing Affordability and related issues.
Housing affordability (HA) may need little motivation. With more than 500,000,000
entries in Google, HA is probably qualified as one of the “global concerns.” Since
there are several papers written on this topic recently, and we also touched on thesubject in the previous section, we would only provide a few remarks (Ben-Shahar etal., 2020b; Deng et al., 2019; Gabriel and Painter, 2020; Leung, 2022; Leung andTsang, 2023; Malpezzi, 2023).
First, there are several ideas of HA, and we must be careful to measure HA. For
instance, one commonly used indicator is the median house price-to-income ratio(PIR). In practice, people do not collect information about the PIR for each household
and then compute the median. Instead, they take the ratio of the median house prices
and the median income as a proxy. Hence, when a significant portion of the

There are other research efforts to combine the insights of Alonso and Tiebout, such as de Bartolome andRoss (2003). They focus on the interactions between the inner-city and suburban, which are ex-ante different,
while Hanushek-Yilmaz’s models focus on ex-ante identical communities.
22
See also Ben-Shahar et al. (2020a).
21

population is absorbed by public rental housing, as in the case of Hong Kong, themedian buyer in the private housing market would have a higher income than the
median person of the income distribution. Thus, the median house price ratio to themedian income is a biased indicator of the median PIR. In other words, the housing
unaffordability of economies like Hong Kong is primarily inflated (Leung et al.,2020b).
Moreover, since income and house prices are endogenous variables and evolve withthe economy, the PIR is a stochastic process. It is vital to notice that there is a cyclicalcomponent of the PIR which is mean-reverting (Leung and Tang, 2023). Hence, the"explosive dynamics of house price-to-income ratio" may be exaggerated.
More fundamentally, as Glaeser (2011) observed, the accommodation of humanbeings can be more spread out spatially, yet people choose to live in cities. 23 Cities
can facilitate consumption, human capital accumulation, trading, and production(Behrens et al., 2014; Berliant and Wang, 2019). Some cities could be more efficientin generating production or consumption than others, attracting people to reside there.
Due to natural or artificial reasons, “attractive” cities could be “slow” in generating“enough” housing supply (Green et al., 2005; Gyourko et al., 2013; Saiz, 2010).
Hence, the housing prices and rents in those places would adjust and become a device
to sort people across potential locations of residence (Couture et al., 2021; Durantonand Puga, 2014; Eeckhout et al., 2014; Glaeser and Gottlieb, 2009; Parkhomenko,
2022). In a sense, housing in certain places would always be “unaffordable” to some

Glaeser (2011) states, “Two hundred forty-three million Americans crowd together in the 3 percent of thecountry that is urban…. On a planet with vast amounts of space (all of humanity could fit in Texas—each of us
with a personal townhouse), we choose cities. Although it has become cheaper to travel long distances, or totelecommute from the Ozarks to Azerbaijan, more and more people are clustering closer and closer together inlarge metropolitan areas. Five million more people every month live in the cities of the developing world, and in
2011, more than half the world’s population is urban.”23

people in equilibrium. Even people who could "afford" to live in any city may find it
in their interest to stay away from certain "expensive cities."
There are other senses of HA. For instance, some people could only afford rent but
not own it in certain places. This is the focus of the tenure choice literature, which isbeyond this paper's scope. We have mentioned a few of the contributions in theprevious section, and we highlight a few more here. For instance, Yao (2023)
calibrates a life-cycle model and shows the rising population share of college
graduates and the growing college premium may explain why the homeownership rateof young people has decreased in the United States. In the spatial equilibrium modelof Parkhomenko (2022), when the price-rent and price-wage ratios grow faster inlarge cities than in smaller ones, some middle-income renters would leave large cities
and move to smaller cities to become homeowners.
Couture et al. (2022) study within-city affordability. They show that if households'
preferences are non-homothetic, higher-income people will move to the downtown
where local amenities are available. This encourages developers to increase the supplyof high-quality neighborhoods and, because the land supply is limited, decrease thesupply of low-quality communities, pushing up the rent faced by the lower-income
renters. Their model can explain a significant share of urban gentrification since 1990
and the cross-city spatial sorting between 1990 and 2014.
Favilukis et al. (2022) build a model of overlapping generations of risk-averse agents
who face idiosyncratic income and mortality risks. They can choose among 3
locations for residence: Outside MSA, Gateway MSA (urban core), and Gateway
MSA (suburban area). The city government of Gateway MSA imposes rent
regulations on rent-stabilized (RS) units and constrains the growth rate of rent in RS
units to be below the market counterpart. Developers must produce a certain fraction

of RS units in their development. RS units are allocated through a random lottery
without income being means-tested. Thus, the distortions created by RS units cangrow with the tenure of their tenants. They calibrate the Gateway MSA (urban core)
to match Manhattan of New York City, the Gateway MSA (suburban area) to matchthe other 24 counties of New York MSA, and the Outside MSA to match the next 74
largest MSA of the United States. They conduct a series of policy experiments.
Among other results, they find that expanding the scope of RS units could lead towelfare gains because the "insurance" role of RS units could outweigh the welfare
loss from different distortions.
A common theme among these recent researches is the insight that there is a location
choice among different cities on top of the traditional tenure choice (rent versusown). 24 The idiosyncratic labor income nature is increasingly emphasized on thehousehold side. Thus, policy-relevant affordability analysis calls for increasingly
complicated models.
3.

Expectation and related issues.
Economists have long been aware of the role expectation could play and thepossibility of an "irrational market." 25 Since Agarwal and Varshneya (2022), Kuchler
et al. (2022) recently reviewed the literature on the relationship between the housing
market and expectation; we complement their efforts by providing some simple
remarks.
First, even in a representative agent context, housing market expectation can be a
subtle issue if the monetary policy is endogenous. For instance, in Adam andFor city economies like Hong Kong and Singapore, the city-level choice disappears. And for countries like
China which imposes stringent spatial friction, the city-level choice may also be limited (Deng et al., 2020; Gaiet al., 2021; Garriga et al., 2021).
25
For instance, John Keynes has many famous quotes, such as “markets can remain irrational a lot longer than
you and I can remain solvent.” See John Maynard Keynes’ quotes,
https://www.goodreads.com/author/quotes/159357.John_Maynard_Keynes?page=4
24

Woodford (2021), agents might overestimate the probability of housing shortage due
to distorted beliefs. As a result, they might channel too many resources to housing
construction and under-provide non-durable consumption goods. To correct such
distorted beliefs, the monetary authority might need to respond aggressively to thehouse price increase, even when such a response is unnecessary under rational
expectation. 26
Second, there is considerable discussion on the empirical determinants of house
prices. In particular, is the house price driven by "economic fundamentals" or nonfundamental factors such as market sentiments? 27 It is assumed that economic agentscan read all the economic statistics. However, it might not be accurate. For instance,
Runge and Hudson (2020) find that while the British public understands the inflation
rate, half of them need to understand the meaning of economic growth. Kladivko andÖsterholm (2020) find that the surveyed Swedish can predict the direction ofunemployment but not the direction of inflation. Empirical research also identifies asystematic difference between the views of economists and the general public. 28
These findings are interesting because central banks and international agencies haveproduced many materials, available for download, to assist the general public in
understanding economics and finance. 29
The theory of "rational inattention" provides a potential explanation (Maćkowiak etal., 2021, Sims, 2003). When agents have a limited capacity to possess information,

For empirical evidence of whether and how much the Federal Reserve responds to the stock and housing
prices, see Aastveit et al. (2021), among others.
27
There is a discussion on whether institutions, financial market conditions, and geography should beconsidered "fundamentals." It is beyond the scope of this paper to discuss that literature. Among others, see
Duca et al. (2021), Garriga et al. (2019), Garriga and Hedlund (2020), Gelain et al. (2018), Girardin and Joyeux(2022), Glaeser (2013), Lai and Van Order (2022), Leung and Tang (2022), Leung and Tse (2017), Ling et al.
(2015), Oikarinen et al. (2018), Saiz (2010), Soo (2018), Van Eyden et al. (2022), and the reference therein.
28
For instance, see Brandts et al. (2022), Caplan (2002).
29
For instance, see Giovannini (2008).
26

they pay attention to a specific data set and ignore the other information. An
alternative explanation is that the general public is simply confident that they can
predict better than the economists and hence discredit the economists' advice. For
instance, Goodspeed (2022) finds that “during periods of high inflation and inflation
regime change,…, the average consumer forecast is more accurate than” professional
forecasts. 30 Future research may explore further along these lines.
4.

Regional economy, regional market
As we mentioned earlier, macroeconomics has experienced much change in recent
years. One development is related to the importance of large firms and production
networks. Since Kydland and Prescott (1982), business cycle fluctuations are
explained by aggregate shocks in macroeconomics (King and Rebelo, 1999; Rebelo,2005). Later research suggests that “micro shocks” may also lead to aggregate
fluctuations under at least two situations. First, some firms are disproportionally large.
Second, firms are located in a production network and use one another’s output as
inputs (e.g., Acemoglu et al., 2012, 2017; Baqaee and Farhi, 2020; Carvalho andGabaix, 2013; Carvalho and Grassi, 2019; Gabaix, 2011). Such theories are confirmed
by more recent empirical work (Atalay, 2017; Joya and Rougier, 2019; Su, 2021).
While different firms can form a production network, different regions within acountry can also create a production network. This is indeed another development inmacroeconomics, which is to recognize the importance of spatial differences and theinteractions among regional economies (Chodorow-Reich, 2020; Pinto and Sarte,2022; Redding and Rossi-Hansberg, 2017). For instance, heterogeneous regional
housing markets respond differently to the same national policy, such as the monetary30
Xiao (2022) built a model of heuristic where agents underuse prior probabilities when calculating theposterior probabilities. His model predictions are consistent with the professional forecasters’ estimate of GDP.

policy (Fratantoni and Schuh, 2003). 31 Different cities may have different housing
supply elasticities due to geographical or political-economic reasons (Saiz, 2010).
Leung and Teo (2011) build a multi-region, dynamic stochastic general equilibrium(MR DSGE) model with a representative agent in each region. In that framework,
they show that differences in regional housing supply functions, which lead todifferent regional supply elasticities, can generate non-trivial cross-regional comovement in house prices and investment. On the other hand, Herkenhoff et al.
(2018) built an MR DSGE model with a representative family for the whole country.
They aggregate 48 continent U.S. states into eight regions. They find that“deregulating just California and New York back to their 1980 land-use regulationlevels would raise aggregate productivity by as much as 7% and consumption by asmuch as 5%.” 32
However, these researches did not incorporate the possibility of over-leverage andbankruptcy, which is one of the main drivers of the Great Recession. 33 In a series of
papers, Mian and Sufi (2011, 2014), Mian et al. (2013) explore the relationships
among loan-to-value (LTV) ratio, consumption, and tradeable and non-tradeable
sector employment at the ZIP code level. 34 Their reduced form estimation shows that
communities experiencing a larger deterioration of their household balance sheet
would also experience more job loss in the non-tradeable sector employment anddisplay a higher marginal propensity to consume (MPC) out of housing wealth. Thosecommunities may be more financially constrained. 35 Interestingly, the relationship

See also Lai and van Order (2022).
See also Tonetii (2018).
33
See also Kehoe et al. (2016, 2018).
34
There are more than 41,000 ZIP codes in the USA. See https://facts.usps.com/42000-zipcodes/#:~:text=There%20are%2041%2C683%20ZIP%20Codes%20in%20the%20country.&text=ZIP%20Codes%20range%20from%2000501,to%2099950%20in%20Ketchikan%2C%20AK35
See also Christelis et al. (2021), Kaplan et al. (2014), Kaplan and Violante (2014), among others.
31
32

between housing net worth shock and tradeable sector employment change from 2007
to 2009 is close to zero. Thus, their research establishes a link between the local laborand housing markets. 36
The papers by Mian and Sufi (2011, 2014), Mian et al. (2013) inspire studies ofregional business cycles. For instance, Beraja et al. (2019a) study the monetarytransmission mechanism in an environment where collateral constraint, refinancing ofmortgage, and consumption spending interact. Their structural estimation results
suggest that the refinancing channel is essential. Moreover, "large variation in house
price growth that was strongly correlated with local economic activity meant thatmonetary stimulus largely flowed to the locations that needed it least" (p.180). 37 This
research may serve as a reminder to consider the regional differences in policymaking.
Beraja et al. (2019b) revisit the results of the Mian paper series by constructing abusiness cycle model that matches regional and aggregate data. They show
analytically that the "wage elasticity with respect to the employment change" arefundamentally different at the aggregate and regional level. For instance, the interest
rate would respond to the aggregate shock but not the regional shock. Regional shocks
may be important to the movements in the local labor markets but not the aggregate
counterpart. Based on the structural estimation using both regional and aggregate data,
they find that the regional wage (i.e., at the state level) is much more flexible than theaggregate counterpart. And while the aggregate demand shocks are essential toexplain the aggregate employment during the Great Recession, it is not so in thesubsequent recovery.

36
37

Liu and Williams (2019) also find substantial heterogeneity in states’ response to a federal tax cut.
See also Eichenbaum et al. (2022).

Jones et al. (2022) examine the extent to which the shocks to household credit are
responsible for the cross-sectional and aggregate movements of employment andconsumption. Like the Mian paper series, they find that states that experience moresignificant decreases in household debt also experience more considerable reductions
in consumption and employment. Yet there may be other shocks that drive themovements. Jones et al. (2022) proceed in the following ways to quantify theimportance of household credit shocks. They build a structural model in whichhouseholds hit by liquidity shocks would have difficulty extracting home equity for
consumption. In addition, there are other shocks in the model. They include theshocks on the time preference (the degree of patience-ness), the disutility of work (thewillingness to work), housing preference (the desire to trade housing with
consumption goods), and productivity, each with a regional-specific and an aggregate
version. There is also a stochastic disturbance term in the monetary policy. They thenestimate the model to match both regional and aggregate data. 38 They find that for
2007-2010, household credit shocks explain no more than a quarter of the decrease inemployment. On the other hand, for 2007-2012, the household credit shocks could
explain up to 40% of the cross-regional differences in employment and consumption.
Part of the reason is the gradual deleveraging of the households leads to a gradualdecrease in the natural interest rate. Thus, the significance of the housing andmortgage markets may vary over time.
Mabille (2020) builds an overlapping generations model in the spirit of Favilukis et al.
(2022). Agents have a locational choice, which is to choose between two regions(high-price and low-price) that differ in terms of amenities and construction costs.

There are some subtle differences in the estimation methods between Beraja et al. (2019b) and Jones et al.
(2022). Please see the papers for details.
38

Agents also have tenure choice, which is to rent or own. Since income risk is
idiosyncratic and risk-sharing is incomplete, loan-to-value (LTV) and payment-toincome (PTI) constraints can bind; some agents have to rent in equilibrium. The
model is calibrated to match aggregate and regional facts of the United States. The
model naturally mimics that the young homeownership rate drops more in the highprice region than in the low-price counterpart. The counterfactual experiment showsthat place-based subsidies are more effective. Mabille (2020) also shows how we can
combine that study of affordability with recognizing regional market differences.
These papers demonstrate that a new generation of structural macro-housing models
with aggregate shock and regional heterogeneity can complement the large-scale
reduced form estimation. In particular, it helps us to identify to what extent theregional differences are given and to what extent those differences come from
economic agents' decisions. Such an understanding often leads to a more accurate
estimate of what policies can (and cannot) achieve.
There are other significant developments in macro-housing research, such as the issue ofendogenous housing supply (Epple et al., 2010; Fan et al., 2022; Leung et al., 2020a; Sun andYiu, 2022). COVID-19 might also bring both short-term and long-term changes. First, thework-from-home (WFH) movement may change the demand for space (Aksoy et al., 2022;
Van Nieuwerburgh, 2022), etc. Second, the labor supply has decreased (Chetty et al., 2022;
Greenwood et al., 2022). Future research on macro-housing should study the implications of
all these changes.
Theoretical models become increasingly realistic and may generate little analytical results.
Instead, models become more "calibrate-able" and built to match micro data. We might nothave a "paradigm" or a "work-horse" model for the macro-housing literature. Instead, themodel design is influenced by the research questions and data available. At the same time,technology has empowered those models to compute the aggregate implications in terms of
output or consumption, or welfare. Such may be the future macro-housing research.

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