Citation: Li, Rita Yi Man. 2022.
Housing Real Estate Economics and
Finance. Journal of Risk and Financial
Management 15: 121. https://
Received: 28 February 2022
Accepted: 1 March 2022
Published: 4 March 2022
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Risk and Financial
Housing Real Estate Economics and Finance
Rita Yi Man Li
Department of Economics and Finance/Sustainable Real Estate Research Center, Hong Kong Shue Yan University,
Hong Kong; email@example.com
Housing research is one of the hot topics in many countries. This paper provides a quick
review of the housing economics research in the US, Sweden, Latvia, China, Corsica, and Italy
published in this special issue. Bao and Shah studied the effects of home-sharing platforms in general
and the effects of the US’ Airbnb on neighbourhood rent. Wilhelmsson’s results showed that interest
rates directly affected house prices and indirectly affected bank loans in Sweden. Caudill and Mixon
threw light on the relative negotiating power of the buyer and seller as a key element of real estate
price models. ˇ
Cirjevskis presented a real application of “step-by-step” valuation options for real
estate development projects as a managerial risk management tool for similar real estate development
projects in the EU to make investment decisions during COVID-19 and in the post-COVID-19 era.
Pelizza and Schenk-Hoppéused an exponential Ornstein–Uhlenbeck process to model price dynamics
provincially and regionally to estimate the liquidation value.
housing economics; housing finance; home-sharing; investment; real estate price;
Homeownership is the dream in many nations. It is affected by many different factors,
such as generations (Li 2015). Studying the factors that affect housing prices has been
a signiﬁcant focus for many years. Previous research has shed light on housing estates
Li et al. 2021
), landﬁll externalities (Li and Li 2018), and exchange rates (Li and Chau 2016).
These estate economics and ﬁnance issues were mainly determined based on housing
economics and ﬁnance research in the US, Sweden, Latvia, China, Corsica, and Italy. This
research covered short-term accommodations, such as Airbnbs in the US, the interplay
between housing and the ﬁnancial market in Sweden, the negotiating power of the buyer
and seller in the Corsican apartment market, urban leverage on housing price in China,
and real-option valuation application for real estate development projects.
Bao and Shah (2020) explored the effects of home-sharing platforms in general and the
effects of Airbnb on neighbourhood rent. Using consumer-facing Airbnb data from the US
gathered between 2013–2017 and rental data from the American online real estate database
Zillow, the study explores Airbnb penetration and rental rates. The ﬁndings indicated that
Airbnb’s impact on rent depends on the individual features of a neighbourhood. The study
urged that policymakers should implement tailored solutions to reduce the platform’s
negative impacts while taking advantage of its economic beneﬁts.
Wilhelmsson (2020)’s research attempted to answer the question of what role the
housing market plays in the transmission mechanism and does the impact remain consistent
over time? The research question considered the importance of the housing market for the
transportation system by using an eight-variable structural vector autoregression model
of the Swedish economy from 1993 to 2018, covering the internet bubble and the ﬁnancial
crises in 2000 and 2008 respectively. The results showed that interest rates directly affected
house prices and indirectly affected bank loans. In addition, the role of banking credit has
grown over time. There had been substantial growth in household debt in Sweden and
elsewhere. Based on the results, it is possible to assess and anticipate the potential effects
of housing prices when interest rates change.
J. Risk Financial Manag. 2022,15, 121. https://doi.org/10.3390/jrfm15030121 https://www.mdpi.com/journal/jrfm
J. Risk Financial Manag. 2022,15, 121 2 of 3
Caudill and Mixon (2020) shed light on the relative negotiating power of the buyer
and seller as a key element of real estate price models. Conventional research on real estate
had sought to examine trading effects in hedonic regression models. Previous research
proposed a procedure for estimating negotiation effects in hedonic regression models that
critically depended on substitution to remove bias from the omitted variables. This study
showed that the proposed solution that was often cited in the real estate economics litera-
ture does not solve the problem of omitted variables. Both models produced bias estimates
of bargaining power when speciﬁc property characteristics were omitted. A classic hedo-
nic regression model of real estate prices using Corsican apartment data supports their
assertion, even when the symmetry of the trading power hypothesis is relaxed.
Urban leverage hurt housing prices by inﬂuencing credit availability. Using panel
data and hedonic models from 236 cities, Lu and Shen (2022) found that urban leverage
negatively impacted housing prices in ﬁrst- and second-tier cities, while no impacts were
observed on third fourth-tier cities. Furthermore, the difference analysis indicated that
purchasing restraint policies amplify the negative effect of urban leverage on housing
prices. In short, urban leverage is an important determinant of housing prices in China.
Cirjevskis (2021) found that the EU real estate market had undergone a severe global
ﬁnancial crisis in 2008–2009 and that it later successfully recovered. Nevertheless, these
markets experienced signiﬁcant uncertainty due to COVID-19 and increased housing mar-
ket volatility. This research shed light on the Latvian real estate market, which experienced
similar economic uncertainty. It presented a real application of “step-by-step” valuation
options for real estate development projects as a managerial risk management tool for
similar real estate development projects in the EU to make investment decisions during
COVID-19 and in the post-COVID-19 era.
Pelizza and Schenk-Hoppé(2020) provided the expected recovery rates for Italian
defaulted mortgages backed by residential or commercial real estate. They used an expo-
nential Ornstein–Uhlenbeck process to model price dynamics provincially and regionally
to estimate the liquidation value. Their ﬁndings showed that rating agencies such as
Moody’s, which used geometric Brownian movement to model the price dynamics, had
higher recovery rates. Hence, the non-performing mortgages held by Italian banks might
Mohammad Ebrahimzadeh Sepasgozar et al. (2020) noted that one of the most crit-
ical challenges for e-government and e-banking is accurately estimating the factors that
signiﬁcantly affect impact customer behaviour. Without knowing these factors, it would
be impossible to predict new the acceptance of new services and acquire a competitive ad-
vantage. This research aimed to identify user intentions using the Technology Acceptance
Model, technology adoption theory, technology diffusion theory, and planned behaviour
theory in the Mehr Bank in Iran. The data were collected through questionnaires from two
hundred clients and employees who worked at or had business dealings with Mehr Bank.
The results conﬁrmed the direct impact of “perceived utility” and “perceived ease of use”
on user attitudes.
Funding: This research received no external funding.
Conﬂicts of Interest: The author declares no conﬂict of interest.
Bao, Helen X. H., and Saul Shah. 2020. The Impact of Home Sharing on Residential Real Estate Markets. Journal of Risk and Financial
Management 13: 161. [CrossRef]
Caudill, Steven B., and Franklin G. Mixon. 2020. Estimating Bargaining Power in Real Estate Pricing Models: Conceptual and Empirical
Issues. Journal of Risk and Financial Management 13: 105. [CrossRef]
Cirjevskis, Andrejs. 2021. Value Maximizing Decisions in the Real Estate Market: Real Options Valuation Approach. Journal of Risk and
Financial Management 14: 278. [CrossRef]
Li, Na, Rita Yi Man Li, and Ruihui Pu. 2021. What is in a name? A modern interpretation from housing price in Hong Kong. Paciﬁc Rim
Property Research Journal 27: 55–74. [CrossRef]
J. Risk Financial Manag. 2022,15, 121 3 of 3
Li, Rita Yi Man. 2015. Generation X and Y’s demand for homeownership in Hong Kong. Paciﬁc Rim Property Research Journal 21: 15–36.
Li, Rita Yi Man, and Kwong Wing Chau. 2016. Econometric Analyses of International Housing Markets. London: Routledge.
Li, Rita Yi Man, and Herru Ching Yu Li. 2018. Have Housing Prices Gone with the Smelly Wind? Big Data Analysis on Landﬁll in
Hong Kong. Sustainability 10: 341. [CrossRef]
Lu, Wanying, and Jianfu Shen. 2022. Urban Leverage and Housing Price in China. Journal of Risk and Financial Management 15: 87.
Mohammad Ebrahimzadeh Sepasgozar, Fatemeh, Usef Ramzani, Sabbar Ebrahimzadeh, Sharifeh Sargolzae, and Samad Sepasgozar.
2020. Technology Acceptance in e-Governance: A Case of a Finance Organization. Journal of Risk and Financial Management 13: 138.
Pelizza, Michela, and Klaus R. Schenk-Hoppé. 2020. Pricing Defaulted Italian Mortgages. Journal of Risk and Financial Management
13: 31. [CrossRef]
Wilhelmsson, Mats. 2020. What Role Does the Housing Market Play for the Macroeconomic Transmission Mechanism? Journal of Risk
and Financial Management 13: 112. [CrossRef]