Article
COVID-19 Infects Real Estate Markets: Short and
Mid-Run Eects on Housing Prices in Campania
Region (Italy)
Vincenzo Del Giudice, Pierfrancesco De Paola * and Francesco Paolo Del Giudice
Department of Industrial Engineering, University of Naples “Federico II”, Piazzale Vincenzo Tecchio 80,
80125 Naples, Italy; vincenzo.delgiud[email protected] (V.D.G.); [email protected] (F.P.D.G.)
* Correspondence: [email protected]
Received: 22 May 2020; Accepted: 2 July 2020; Published: 5 July 2020
Abstract: The COVID-19 (also called “SARS-CoV-2”) pandemic is causing a dramatic reduction in
consumption, with a further drop in prices and a decrease in workers’ per capita income. To this will be
added an increase in unemployment, which will further depress consumption. The real estate market,
as for other productive and commercial sectors, in the short and mid-run, will not tend to move
independently from the context of the aforementioned economic variables. The eect of pandemics
or health emergencies on housing markets is an unexplored topic in international literature. For this
reason, firstly, the few specific studies found are reported and, by analogy, studies on the eects
of terrorism attacks and natural disasters on real estate prices are examined too. Subsequently,
beginning from the real estate dynamics and economic indicators of the Campania region before
the COVID-19 emergency, the current COVID-19 scenario is defined (focusing on unemployment,
personal and household income, real estate judicial execution, real estate dynamics). Finally, a real
estate pricing model is developed, evaluating the short and mid-run COVID-19 eects on housing
prices. To predict possible changes in the mid-run of real estate judicial execution and real estate
dynamics, the economic model of Lotka–Volterra (also known as the “prey–predator” model) was
applied. Results of the model indicate a housing prices drop of 4.16% in the short-run and 6.49% in
the mid-run (late 2020–early 2021).
Keywords: COVID-19; SARS-CoV-2; pandemic; unemployment; personal and household income; real
estate judicial execution; real estate dynamics; housing prices; Campania region; Lotka–Volterra model
1. Introduction
COVID-19 is an ongoing pandemic of coronavirus disease 2019, caused by severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2). The outbreak was first identified inWuhan, China, in December
2019. TheWorld Health Organization declared the outbreak a Public Health Emergency of International
Concern on 30 January, and a pandemic on 11 March (World Health Organization 2020).
On 9 March 2020, Italian government imposed a “lockdown” in the country for the COVID-19
pandemic. After about two months of lockdown, and a further two months of gradual recovery of
production and work activities, the pandemic’s economic eects begin to appear in all their intensity.
The pandemic has caused severe global economic disruption, including the largest global recession
since the U.S. Great Depression (International Monetary Fund 2020). Real estate markets are not or
will remain immune from the eects of the COVID-19 pandemic.
A nefarious scenario recalls a question that many ask themselves: will house prices drop? In Italy,
the drop in the number of real estate transactions is a certainty, but it will not be accompanied by an
equal drop in prices, at least not in the short term. This is what Nomisma and Scenari Immobiliari
Soc. Sci. 2020, 9, 114; doi:10.3390/socsci9070114 www.mdpi.com/journal/socsci
Soc. Sci. 2020, 9, 114 2 of 18
say, two big independent and private Italian real estate research institutes (Nomisma 2020; Scenari
Immobiliari 2020).
There are two factors to be reckoned: on the one hand, the impoverishment that will result as an
eect induced by involuntary inactivity for many productive and commercial sectors; on the other,
a new future propensity of families who will give priority to saving to protect themselves from
other future diculties. In the short term, the eect of this situation will reflect quickly on housing
sales and real estate prices, which for the residential market, could drop between 1.3% and 4.0%
in the two-year period 2020–2021, and then, slightly rise in 2022, according to what the Nomisma
Institute arms (Nomisma 2020). The real estate outlook of the Scenari Immobiliari group, updated to
April 2020, indicates an estimated 2.1% reduction in real estate prices (?1.2% for Naples, the capital
city of Campania) (Scenari Immobiliari 2020). Since the end of the Italian lockdown, real estate data
sourced by “Idealista”, one of Italy’s most important websites for real estate ads, have recorded
a reduction in the requested home prices in the order of 4.4% for Naples (capital of the Campania
region) (Idealista 2020).
The COVID-19 pandemic is causing a dramatic reduction in consumption, with a further drop
in prices and a decrease in workers’ per capita income. To this will be added to an increase in
unemployment, which will further depress consumption.
The real estate market, as for other productive and commercial sectors, in the medium-long term,
will not tend to move independently from the context of the aforementioned macroeconomic variables.
At the sector level, therefore, only a few dierent possible scenarios can be proposed: the pre-virus
one, for which we were preparing but was overcome by the events, a soft scenario and a pessimistic
one, the latter in which the unemployment rate could increase exponentially.
In such a changing scenario, such as the one marked by the current health and economic emergency
in progress, the only way to make predictions is to proceed by hypothesis.
In line with these perspectives, our aim is to provide a short and mid-run forecast of housing prices
in the Campania region (Italy), beyond the levels that can be estimated from cyclical housing market
downturns. Under a pandemic that exploded almost instantly, homeowners experience a dramatic loss
of real estate capital, a significant economic shock to the local and regional economy, negative health
impacts, and in many cases, forced evacuation and relocation during periods of decontamination and
disinfection (Giesecke et al. 2012).
We investigate the regional economic consequences of the COVID-19 pandemic, taking into
account the ways in which the event might aect regional economic activity: reduction in eective
resource supply and behavioral changes in the perceptions of economic factors. Although a pandemic
causes no physical building damage, it generates a substantial short-run financial liquidity loss due to
businesses interruption. Not only that but also changes in fear and risk perception increase the supply
cost of resources, especially in the areas most aected, while simultaneously reducing demand for real
estate goods. We use results from dierent nationwide surveys on the current COVID-19 scenario to
implement a regressive model focused on real estate values and homeowners’ behavioral eects.
The eect of pandemics or health emergencies on housing markets is an unexplored topic in
international literature. For this reason, firstly, the few specific studies found are reported and, by
analogy, studies on the eects of terrorism attacks and natural disasters on real estate prices are
examined too. Subsequently, beginning from real estate dynamics and macroeconomic indicators of
the Campania region before the COVID-19 emergency, the current COVID-19 scenario is presented
(focusing on employment, personal and family income, real estate judicial execution, and real estate
dynamics). To predict possible changes in the mid-run of real estate judicial execution and real estate
dynamics, the economic model of Lotka–Volterra (also known as the “prey–predator” model) was
applied. Finally, a real estate pricing model is proposed, evaluating the short and mid-run eects on
housing prices.
Soc. Sci. 2020, 9, 114 3 of 18
2. Literature Review
The eect of pandemics or health emergencies on housing markets, as already mentioned, is an
unexplored topic. Only very few specific studies are found in the literature.
In 2008,Wong (2008) investigated how housing markets react to extreme events like the 2003 Hong
Kong Severe Acute Respiratory Syndrome (SARS) epidemic. A panel dataset of large-scale housing
complexes is used to exploit the cross-sectional variation in the spread of SARS to estimate the eect of
the disease on real estate prices and sales. Wong concludes that the average price declines by 1–3% if
a property is directly aected by SARS, and by 1.6% for all properties as a result of the outbreak of
the disease. Concerning the results, he also highlights that the absence of price overreaction is likely to
be related to housing market characteristics.
In 2019, Argyroudis and Siokis (2019) investigated the impact of the sub-prime loan crisis on
the real estate market of Hong Kong and, only marginally, identify periods where the underlying
dynamical structure of the real estate market was impacted by certain events like the SARS epidemic.
In 2020, Nicola et al. (2020) summarized the socioeconomic eects of COVID-19 on individual
aspects of the world economy. Again in 2020, the main findings of the Zillow Economic Research (2020)
on the 2003 Hong Kong SARS epidemic are: 1.75% loss in annualized Gross Domestic Product (GDP), or
5.1% monthly loss at peak, 1.3% increase in unemployment, statistically insignificant 1.9% fall in home
prices, recording a drop transactions by an average of 33% for the duration of the pandemic. The Zillow
Economic Research considers as inputs of its model the GDP, unemployment rate, residential real
estate prices, and real estate transactions.
By analogy, we have also examined studies related to the eects of terrorism attacks and natural
disasters on real estate prices in order to identify which approaches and tools can be adapted to a model
that aims to capture the real estate eects of pandemics or health emergencies events. Although,
apparently, natural disasters seem less comparable to the case of investigation, they may cause
widespread land contamination (as what happened in Fukushima in Japan in March 2011) or also toxic
spills (dioxins, petroleum products, etc.). These phenomena are certainly characterized by stigma
eects, evacuation of the inhabitants, and remediation works extended for many years.
Mills (2002) is the first scholar who studied, in a qualitative manner, the eects of terrorism on U.S.
real estate and urban development, proposing the first ideas about how private people and businesses
will perceive the risks and how quickly and decisively private parties will make adjustments.
Redfearn (2005) studies the relationship among land markets and terrorism, this in respect to
the uncovering perceptions of risk by examining land price changes following 9/11 attacks in U.S.
The attacks on 11 September 2001 oered an opportunity to identify the idiosyncratic influence of
perceived risk from terrorism. In this perspective, Redfearn performed an experiment to assess home
prices before and after the events in order to isolate price changes due to changes in expectations about
future attacks and their impacts on housing values. The hypothesis developed is that consumers have
altered their valuation of risk and this should be testable by examining the price gradient around
possible targets—prices closer to sites of greater perceived risk should become relatively less valuable.
Hazam and Felsenstein (2007) show an inverse relationship between neighborhood house values
and terror events in Jerusalem. They verify the hypothesis that fear is a central factor in understanding
human behavior in the face of terror. This claim is addressed in the context of behavior in the Jerusalem
housing market over the terror-stricken years in the city, 1999–2004. Using a unique data source
and the tools of spatial data analysis, the paper provides support for the above hypothesis in three
aspects. First, patterns of terror in the city are shown to be increasingly deconcentrated over the period
studied. Secondly, the types of terror with the sharpest eect on residential property prices are those
most associated with randomness. Thirdly, the eect of terror is less on purchasing prices than on
rental prices.
Dermisi (2007) identifies the cyclical patterns for the oce market under potential terrorism threat
by comparing vacancy rates and rent per square foot trends before and after 11 September 2001. This
study goes beyond identifying general market trends and focuses specifically on the oce market
Soc. Sci. 2020, 9, 114 4 of 18
trends in Chicago. The findings indicate that the real estate market was severely impacted by 9/11 and
did not recover until the end of 2005. In general, the Chicago oce market cycles were estimated to be
between 6.4 and 13 years.
Later, Abadie and Dermisi (2008) apply property-level data on vacancy and rents to investigate
the impact of terrorism risk following 9/11 on the oce market in downtown Chicago. The 9/11 attacks
induced a large increase in the perception of terrorist risk in the Chicago Central Business District,
which includes the tallest building in the U.S. (the Sears Tower) and other landmark buildings, which
are potential targets of large-scale terrorist attacks. Results suggest that economic activity in the Central
Business Districts can be greatly aected by changes in the perceived level of terrorism.
Arbel et al. (2010) present new findings on the economic cost of terror. In particular, the paper
provides evidence of changes in house prices in the Gilo neighborhood of Jerusalem in the wake of
the 2000 Second Palestinian Intifada, using a real estate sample of 555 housing transactions from the Gilo
neighborhood of Jerusalem over the 1997–2008 period. Results indicate that shooting events result in
a lagged 12% reduction in Gilo house values. However, as evidenced in the impulse response functions,
those eects are largely reversed within 18 months of the terror event. Again, highlighted is an
average quality-adjusted house price decline of about 10% among “frontline” relative to “non-frontline”
dwellings in Gilo in the aftermath of the outbreak of hostilities; moreover, much of that eect persisted
some five years subsequent to the cessation of violence.
Besley and Mueller (2012) exploit data on the pattern of violence across regions and over time
to estimate the impact of the peace process in Northern Ireland on house prices. After establishing
a negative correlation between killings and house prices, they estimate the parameters of a Markov
switching model with conflict and peace as latent states, and this model is used to estimate the size of
the peace dividend, as captured in house price changes.
Dormady et al. (2014) provide a model for the economic analysis of the potential consequences of
a simulated anthrax terrorism attack on real estate within the Seattle metropolitan area. In particular,
they have spatially disaggregated the impacts on the median sales price of residential housing within
the Seattle metro area, simulating an attack on the central business district, and found that the median
sales price could decline by as much as 280,000 USD, and by nearly 100,000 USD in nearby communities
(i.e., from 86.80% to 30.99%, approximately).
Concerning the economic eects of natural disasters, Hallstrom and Smith (2005) propose a model
in order to measure, in the Florida counties, the impacts on housing prices of Hurricane Andrew, also
comparing these eects with the hurricane’s direct path. Two regression models, based on repeat
sales prices as the dependent variable, have been implemented. In the
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