scholarly journals Housing Price Index, Wealth, and Protective Shield against Covid-19

SIASAT ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 1-8
Author(s):  
Mihai Pichler ◽  
Florin Skutnik ◽  
Aurel Vlad ◽  
Hossein Shahri ◽  
Muhammad Ridwan
Keyword(s):  
Price Index ◽  
Housing Price ◽  
The Other ◽  
Small City ◽  
Housing Price Index ◽  
Protective Shield ◽  
The Us ◽  
Price Indexes ◽  
Big City

This paper aims to fill two purposes: 1) we document that housing price index between different cities have inter-correlation. This means that when the housing price in one city goes up the other city follows. However, in the case of a big city and a small city, the housing price index of small city follows the path of housing price index in the small city. 2) The housing price index is a measure of wellbeing and wealth of residents. At the onset of a pandemic, wealthy and richer people have a wealth-protective shield against the disease. We show that this is the case in the US. We document that higher housing price indexes are associated with lower confirmed case of Covid-19 and lower risks of death due to the disease. 

scholarly journals Dynamics of the US Housing Market: A Quantal Response Statistical Equilibrium Approach

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 831 ◽  
Author(s):  
Özlem Ömer
Keyword(s):  
Housing Market ◽  
Maximum Entropy ◽  
Price Index ◽  
Housing Price ◽  
Statistical Equilibrium ◽  
Market Dynamics ◽  
Quantal Response ◽  
Housing Price Index ◽  
The Us ◽  
Equilibrium Approach

In this article, we demonstrate that a quantal response statistical equilibrium approach to the US housing market with the help of the maximum entropy method of modeling is a powerful way of revealing different characteristics of the housing market behavior before, during and after the recent housing market crash in the US. In this line, a maximum entropy approach to quantal response statistical equilibrium model (QRSE) is employed in order to model housing market dynamics in different phases of the most recent housing market cycle using the S&P Case Shiller housing price index for 20 largest- Metropolitan Regions, and Freddie Mac housing price index (FMHPI) for 367 Metropolitan Cities for the US between 2000 and 2015. Estimated model parameters provide an alternative way to understand and explain the behaviors of economic agents, and market dynamics by questioning the traditional economic theory, which takes assumption for the behavior of rational utility maximizing representative agent with self-fulfilled expectations as given.

scholarly journals REGIONAL HOUSE PRICE INDEX CONSTRUCTION – THE CASE OF SWEDEN

2013 ◽  
Vol 17 (3) ◽  
pp. 278-304 ◽  
Author(s):  
Lars-Erik Ericson ◽  
Han-Suck Song ◽  
Jakob Winstrand ◽  
Mats Wilhelmsson
Keyword(s):  
Labor Markets ◽  
Price Index ◽  
House Price ◽  
Housing Price ◽  
Standard Errors ◽  
Academic Literature ◽  
Housing Price Index ◽  
Thin Markets ◽  
Price Indexes ◽  
Open Question

The academic literature on the construction of regional house price indexes usually uses geographic areas whose boundaries are administratively drawn. However such administrative regions might not be optimal for the construction of regional price indexes. When producing housing price indexes, we often encounter problems with insufficient number of observations. One way to remedy this problem is to estimate a quarterly index instead of a monthly index. Another possible way to mitigate the thin markets problem is to construct indexes for geographically aggregated regions. However, the literature that discusses methods of dealing with the problem of thin markets and especially geographical aggregation is very rare. The goal of this paper is to construct a housing price index for a major part of Sweden, and to construct price index series for a number of regions. The number of regions, and how their boundaries should be created in order to construct reliable regional price indexes, is however an open question. We apply traditional hedonic methodology in order to estimate house price indexes for both predefined regions whose boundaries are based on a division of labor markets in Sweden, as well as a division of regions based on statistical cluster analysis. The results from this study suggest that regions should be clustered together based on regional price levels and/or price development as clustering variables. If only geographical proximity is used as clustering variable, our computations show that there is a high risk that we end up with some clusters having large standard errors, which in turn might result in inaccurate indexes.

US cities’ housing squeeze may prove hard to solve

2018 ◽  
Keyword(s):  
Housing Market ◽  
Housing Stock ◽  
Low Cost ◽  
Housing Price ◽  
Office Work ◽  
Content Type ◽  
Housing Price Index ◽  
Us Economy ◽  
Safety Risks ◽  
The Us

Subject US housing market. Significance The Case-Shiller 20-city composite housing price index hit a record high in May 2018, surpassing its previous record in 2006. However, unlike the mid-2000s, evidence suggests that the US housing market is not in a national bubble. Instead, prices are high in many cities due to an undersupply of housing. This has wider effects: by one estimate, without current land restrictions on housing development, the US economy would be 9% larger than it is now. Impacts Low-cost cities will increasingly be back-office work destinations. If rents and housing costs keep rising, consumers may make greater use of credit, raising indebtedness risks. Greater reliance on older US housing stock will mean greater maintenance costs and safety risks. The housing debate will largely be a cross-party rather than partisan issue.

Testing the Predictability of U.S. Housing Price Index Returns Based on an IVX-AR Model

2020 ◽  
Vol 115 (532) ◽  
pp. 1598-1619 ◽  
Author(s):  
Bingduo Yang ◽  
Wei Long ◽  
Liang Peng ◽  
Zongwu Cai
Keyword(s):  
Price Index ◽  
Housing Price ◽  
Ar Model ◽  
Housing Price Index
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