scholarly journals The Urban Blight Costs in Taiwan

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Chich-Ping Hu ◽  
Tai-Shan Hu ◽  
Peilei Fan ◽  
Hai-Ping Lin
Keyword(s):  
Real Estate ◽  
Urban Space ◽  
Housing Prices ◽  
Spillover Effect ◽  
Well Being ◽  
Hedonic Model ◽  
Urban Blight ◽  
The Government ◽  
Real Estate Prices

Urban blight is not only an eyesore for city residents, but also a threat to health, psychological well-being, and safety. It not only represents substantial economic decline, but also spreads through urban space. As well as the loss of personal property value, urban blight also harms public interests in the public domain. This study finds that danger and age are the two main factors of urban blight. Ignoring these two factors causes housing prices to fall. The decline in population due to long-term economic stagnation and the exodus of residents and industries, coupled with the long-term decline in income and spending on maintenance of old houses, has led to major visual and physical economic blight. This investigation adopts the hedonic model to analyze the correspondence of house prices with urban blight, based on real estate prices and related township variables announced by the government in Taiwan in 2017, and applies the spatial regression model to investigate the direct and indirect effects of real estate prices. The following conclusions can be drawn from the analytical results. 1. The spatial lag model finds that urban blight has a spatial spillover effect. 2. The government must not disregard the blight, due to its detrimental effect on housing prices and spatial diffusion effect. 3. The factors that affect the blight are age of residents, age of buildings, poverty, and danger.

scholarly journals The Influence Of Financing On The Dynamics Of Housing Prices

2014 ◽  
Vol 14 (2) ◽  
pp. 101-113
Author(s):  
Mirosław Bełej ◽  
Sławomir Kulesza
Keyword(s):  
Real Estate ◽  
Open Systems ◽  
Housing Prices ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
The Real Estate Market ◽  
Real Estate Prices ◽  
National Housing

Abstract Real estate market can be thought of as an open, dynamic system. It means that it is able to exchange stimuli with other open systems, and that its state evolves in a way that might be described mathematically. It turns out that two main processes contribute to the overall evolution of the real estate market: long-term, predictable evolution, interrupted by sharp changes of catastrophic origin. In this picture, national housing funds play an important role in supporting the housing finance: on one hand they could either stimulate or suppress the real estate market influencing the availability of the mortgage credit, but on the other hand, they could also help to stabilize prices. In this study, an attempt was made to determine the degree of relationship between the volume of mortgage financing from national housing funds and the dynamics of real estate prices.

Import obstacles will slow Argentine economic rebound

2021 ◽  
Keyword(s):  
Spillover Effect ◽  
Exchange Market ◽  
Luxury Goods ◽  
Content Type ◽  
Product Availability ◽  
Regulatory Changes ◽  
High Inflation ◽  
Red Tape ◽  
The Government

Significance In January, the Central Bank of Argentina restricted access to the official exchange market for imports of some luxury goods, while the government asked companies to present their foreign trade estimates for 2021 and suggested that it would not approve any rise in imports unless this was offset with higher exports. Importers are facing mounting delays, which raise costs and hamper domestic production by restricting access to inputs. Impacts Higher import costs due to red-tape delays and shortages of product availability will fuel already high inflation. Frequent regulatory changes will discourage long-term investments and damage importers’ relations with foreign suppliers. Import controls will hit the auto sector hard, with a negative spillover effect in manufacturing more broadly.

Property investor decisions using income and rental ratio signals

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Billie Ann Brotman
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Discount Rate ◽  
Price Index ◽  
Housing Prices ◽  
Housing Price ◽  
Consumer Sentiment ◽  
Content Type ◽  
Income Ratio ◽  
Real Estate Prices

PurposeThis paper, a case study, aims to consider whether the income ratio and rental ratio tracks the formation of residential housing price spikes and their collapse. The ratios are measuring the risk associated with house price stability. They may signal whether a real estate investor should consider purchasing real property, continue holding it or consider selling it. The Federal Reserve Bank of Dallas (Dallas Fed) calculates and publishes income ratios for Organization for Economic Cooperation and Development countries to measure “irrational exuberance,” which is a measure of housing price risk for a given country's housing market. The USA is a member of the organization. The income ratio idea is being repurposed to act as a buy/sell signal for real estate investors.Design/methodology/approachThe income ratio calculated by the Dallas Fed and this case study's ratio were date-stamped and graphed to determine whether the 2006–2008 housing “bubble and burst” could be visually detected. An ordinary least squares regression with the data transformed into logs and a regression with structural data breaks for the years 1990 through 2019 were modeled using the independent variables income ratio, rent ratio and the University of Michigan Consumer Sentiment Index. The descriptive statistics show a gradual increase in the ratios prior to exposure to an unexpected, exogenous financial shock, which took several months to grow and collapse. The regression analysis with breaks indicates that the income ratio can predict changes in housing prices using a lead of 2 months.FindingsThe gradual increases in the ratios with predetermine limits set by the real estate investor may trigger a sell decision when a specified rate is reached for the ratios even when housing prices are still rising. The independent variables were significant, but the rent ratio had the correct sign only with the regression with time breaks model was used. The housing spike using the Dallas Fed's income ratio and this study's income ratio indicated that the housing boom and collapse occurred rapidly. The boom does not appear to be a continuous housing price increase followed by a sudden price drop when ratio analysis is used. The income ratio is significant through time, but the rental ratio and Consumer Sentiment Index are insignificant for multiple-time breaks.Research limitations/implicationsInvestors should consider the relative prices of residential housing in a neighborhood when purchasing a property coupled with income and rental ratio trends that are taking place in the local market. High relative income ratios may signal that when an unexpected adverse event occurs the housing market may enter a state of crisis. The relative housing prices to income ratio indicates there is rising housing price stability risk. Aggregate data for the country are used, whereas real estate prices are also significantly impacted by local conditions.Practical implicationsRatio trends might enable real estate investors and homeowners to determine when to sell real estate investments prior to a price collapse and preserve wealth, which would otherwise result in the loss of equity. Higher exuberance ratios should result in an increase in the discount rate, which results in lower valuations as measured by the formula net operating income dividend by the discount rate. It can also signal when to start reinvesting in real estate, because real estate prices are rising, and the ratios are relative low compared to income.Social implicationsThe graphical descriptive depictions seem to suggest that government intervention into the housing market while a spike is forming may not be possible due to the speed with which a spike forms and collapses. Expected income declines would cause the income ratios to change and signal that housing prices will start declining. Both the income and rental ratios in the US housing market have continued to increase since 2008.Originality/valueA consumer sentiment variable was added to the analysis. Prior researchers have suggested adding a consumer sentiment explanatory variable to the model. The results generated for this variable were counterintuitive. The Federal Housing Finance Agency (FHFA) price index results signaled a change during a different year than when the S&P/Case–Shiller Home Price Index is used. Many prior studies used the FHFA price index. They emphasized regulatory issues associated with changing exuberance ratio levels. This case study applies these ideas to measure relative increases in risk, which should impact the discount rate used to estimate the intrinsic value of a residential property.

Real effects of real estate: evidence from unemployment rates

2020 ◽  
Vol 37 (4) ◽  
pp. 605-623
Author(s):  
Can Dogan ◽  
John Can Topuz
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Economy ◽  
Residential Real Estate ◽  
Content Type ◽  
Unemployment Rates ◽  
The Real ◽  
Real Estate Price ◽  
Real Effects ◽  
Real Estate Prices

Purpose This paper aims to investigate the relationship between residential real estate prices and unemployment rates at the Metropolitan Statistical Area (MSA) level. Design/methodology/approach This paper uses a long time-series of MSA-level quarterly data from 1990 to 2018. It uses an instrumental variable approach to estimate the effects of residential real estate prices on unemployment rates using the geography-based land constraints measure of Saiz (2010) as the instrument. Findings The results show that changes in residential real estate prices do not have a causal effect on unemployment rates in the same quarter. However, it takes 9-12 months for an increase (decrease) in real estate prices to decrease (increase) unemployment rates. This effect is significant during both pre- and post-financial crisis periods and robust to control for the economic characteristics of MSAs. Research limitations/implications This paper contributes to the emerging literature that studies the real effects of real estate. Particularly, the methodology and the findings can be used to investigate causal relationships between housing prices and small business development or economic growth. The findings are also of interest to policymakers and practitioners as they illustrate how and when real estate price shocks propagate to the real economy through unemployment rates. Practical implications This study’s findings have important implications for academics, policymakers and investors as they provide evidence of a snowball effect associated with shocks to real estate prices: increasing (decreasing) unemployment rates following a decrease (increase) in real estate prices exacerbates the real estate price movements and their economic consequences. Originality/value This paper analyzes a significantly longer period, from 1990 to 2018, than the existing literature. Additionally, it uses the MSA-level land unavailability measure of Saiz (2010) as an instrument to explore the effects of residential real estate prices on unemployment rates and when those effects are observed in the real economy.

scholarly journals COVID-19 Infects Real Estate Markets: Short and Mid-Run Effects on Housing Prices in Campania Region (Italy)

2020 ◽  
Vol 9 (7) ◽  
pp. 114 ◽  
Author(s):  
Vincenzo Del Giudice ◽  
Pierfrancesco De Paola ◽  
Francesco Paolo Del Giudice
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Real Estate Market ◽  
Campania Region ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Short Run ◽  
Predator Model ◽  
Real Estate Prices

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 effect 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 effects 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 effects 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).

scholarly journals Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices

2020 ◽  
Vol 12 (14) ◽  
pp. 5679 ◽  
Author(s):  
Yunjong Kim ◽  
Seungwoo Choi ◽  
Mun Yong Yi
Keyword(s):  
Real Estate ◽  
Traditional Approach ◽  
Housing Prices ◽  
Capital City ◽  
Housing Type ◽  
Residential Housing ◽  
Machine Learning Approach ◽  
Price Estimation ◽  
Real Estate Prices ◽  
Current Price

In this paper, we propose a novel procedure designed to apply comparable sales method to the automated price estimation of real estates, in particular, that of apartments. Apartments are the most popular residential housing type in Korea. The price of a single apartment is influenced by many factors, making it hard to estimate accurately. Moreover, as an apartment is purchased for living, with a sizable amount of money, it is mostly traded infrequently. Thus, its past transaction price may not be particularly helpful to the estimation after a certain period of time. For these reasons, the up-to-date price of an apartment is commonly estimated by certified appraisers, who typically rely on comparable sales method (CSM). CSM requires comparable properties to be identified and used as references in estimating the current price of the property in question. In this research, we develop a procedure to systematically apply this procedure to the automated estimation of apartment prices and assess its applicability using nine years’ real transaction data from the capital city and the most-populated province in South Korea and multiple scenarios designed to reflect the conditions of low and high fluctuations of housing prices. The results from extensive evaluations show that the proposed approach is superior to the traditional approach of relying on real estate professionals and also to the baseline machine learning approach.

Housing Market Hedonic Price Study Based on Boosting Regression Tree

2017 ◽  
Vol 21 (6) ◽  
pp. 1040-1047 ◽  
Author(s):  
Guangtong Gu ◽  
Bing Xu ◽  
◽  
◽  
Keyword(s):  
Real Estate ◽  
Housing Prices ◽  
Regression Tree ◽  
Gradient Boosting ◽  
Hedonic Price ◽  
Tree Model ◽  
Policy Makers ◽  
Real Estate Markets ◽  
Architectural Features ◽  
Real Estate Prices

Based on the purchase price data of new real estate markets three cities in China, Beijing, Shanghai, and Guangzhou, including architectural features, neighborhood property features, and location features, in this study a boosting regression tree model was built to study the factors and the influence path of housing prices from the microcosmic perspective. First, a classical hedonic price model was constructed to analyze and compare the significant effect factors on housing prices in the market segments of the three cities. Second, the gradient boosting regression tree method that is proposed in this paper was applied to the three markets in combination to analyze the influence paths and factors and the importance of the type of housing hedonic price. The influence paths of housing hedonic prices and decision tree rules are visualized. The significant housing features are effectively extracted. Finally, we present three main conclusions and several suggestions for policy makers to improve urban functions while stabilizing real estate prices.

Research on the Impacts of Real Estate on Economic Growth: A Theoretical Model-Based Analysis

2018 ◽  
Vol 06 (04) ◽  
pp. 1850025
Author(s):  
Xiaoxi ZHANG ◽  
Lu GUO
Keyword(s):  
Economic Growth ◽  
Real Estate ◽  
Low Income ◽  
Housing Prices ◽  
Well Being ◽  
Real Estate Market ◽  
The Real ◽  
The Real Estate ◽  
Pillar Industry ◽  
The Impact

As the pillar industry of China’s economy, the real estate sector has a significant impact on macroeconomic growth. We assume that the first stage of economic actors’ working lives is a low-income one, while their second stage is a high-income one. Then, relying on an Overlapping-Generations Model, we analyze how, via real estate, the behaviors of different income groups affect the macroeconomy. The results show that when the supply of real estate market fluctuates then this has an impact on economic growth, but the extent of the impact depends on the relationship between the real estate and the consumer markets. We also find that when economic actors more greatly prefer their current situations of well-being, no matter whether there takes place or not a new increase in real estate stocks, a negative correlation will exist in the relation between real estate stocks and their prices. Lastly, we come to the conclusion that increases in property taxes can effectively reduce housing prices, but the impact of transaction taxes on housing prices can still not be determined.

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