Is HOA capitalized in housing price? Evidence from Chongqing, China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Feng Deng
Keyword(s):  
Telephone Survey ◽  
Empirical Finding ◽  
Housing Price ◽  
Data Set ◽  
Chinese Cities ◽  
Content Type ◽  
Dummy Variable ◽  
Homeowners Association ◽  
Private Communities ◽  
Different Sources

Purpose International research has found that Homeowners Association (HOA) is capitalized in housing price in the West. Is that result applicable in Chinese cities? In China there is also widespread applause for HOA. Will that leave trail in the housing market? This paper aims to answers these questions by presenting empirical evidence from 113 private gated communities in Chongqing, China. Design/methodology/approach The data set comes from three different sources including a telephone survey. The research methodology includes hedonic models with an endogenous dummy variable of the presence of HOA in a community. Findings HOA is not capitalized in housing price. Research limitations/implications The empirical finding helps to explain why about 80% of private communities in big Chinese cities have not formed an HOA. Originality/value This is the first empirical study on HOA capitalization in housing price in China.

Green federal tax credits impact on US housing prices

2020 ◽  
Vol 13 (4) ◽  
pp. 553-564
Author(s):  
Billie Ann Brotman
Keyword(s):  
Regression Model ◽  
Energy Policy ◽  
Income Tax ◽  
Housing Prices ◽  
Housing Price ◽  
Tax Credits ◽  
Disposable Income ◽  
Content Type ◽  
Dummy Variable ◽  
Energy Policy Act

Purpose The purpose of this study is to investigate whether increases in homeowner green amenities occurred because of income tax credits to the degree that changes in housing prices are measurable. Are higher incomes, lower mortgage rates and green income-tax credits impacting housing price changes? Design/methodology/approach The paper uses the least-squares regression model with natural log specifications. The log of income and a dummy variable, which was assigned to the Energy Policy Act (2005) and the American Recovery and Reinvestment Act (2009) coverage dates are used as independent variables. Two regression models were examined using monthly housing price data from January 1990 through the year 2018. The first regression model used a single dummy variable for credits available under the Policy Act of 2005 and the Recovery Act of 2009. The second regression model considered the credits granted under these two laws separately. Disposable income per capita impacts demands for housing while green upgrade expenditures affect the cost of housing. Findings The laws set low credit limits of $500 followed by $1,500 but because of the multiplier effect, the spending appears to have magnified and been much higher. The credit availability variables have positive coefficients and were significant at 1 per cent. This implies that single-family housing prices were sensitive to the existence of residential energy property income-tax credits. The R2 results were 0.93 or above for both models. Research limitations/implications The data used was aggregated and publicly available online. Many studies use aggregated macroeconomic data when modeling housing prices using the exogenous variable of disposable income but there is no substitute for examining individual homes by location and their sales price to see under what conditions green income-tax credits have the most impact. There could be demographic issues that are missed when using aggregated information. Practical implications Spending on heating/cooling systems, dual pane windows and other green amenities keeps the housing stock modernized and housing prices steady or rising. An additional benefit is that spending motivated by self-interest can simulate household consumption spending. Houses deteriorate due to wear and tear. Physical-repairable depreciation represents a situation where maintenance funds are continuously needing to be spent. Repairs and upgrades to the structure of the property keep its price stable by stopping the physical depreciation that would otherwise occur with the passage of time. Social implications The paper provides support for the idea that residential green amenity upgrades positively impact the value of a house. These green-amenity upgrades, which other research studies have suggested should be included explicitly in the appraisal process, are a major characteristic of a property when a price estimate is being done. Housing being sold should have a section on the information sheet noting the property green upgrades that exist and an energy efficiency score should be assigned to each house listed for sale. Originality/value There are few (if any) academic research papers studying the impact of green tax credits available under the Energy Policy Act (2005) and under the American Recovery and Reinvestment Act (2009). The degree to which green income-tax credits stimulate spending on housing has not been addressed by researchers. This paper is an initial research attempt to quantify whether these legislative efforts measurably encouraged homeowners to adopt newer, greener technologies.

R & D collaboration in the automotive innovation environment

2015 ◽  
Vol 53 (6) ◽  
pp. 1224-1246 ◽  
Author(s):  
Lara Agostini ◽  
Federico Caviggioli
Keyword(s):  
Web Sites ◽  
Data Set ◽  
Relationship Type ◽  
Content Type ◽  
Technological Value ◽  
Automated Algorithm ◽  
Area Of Interest ◽  
Innovation Output ◽  
Patented Inventions ◽  
Different Sources

Purpose – The purpose of this paper is twofold: to analyze to what extent innovation output of R & D collaborations, proxied by co-patenting activities in terms of quantity, characteristics and value, differs depending on whether the engaged R & D partners have a certain type of relationship (allies, suppliers and subsidiaries); to identify possible automakers co-patenting patterns taking into account the differences in the innovation output with their R & D partners. Design/methodology/approach – To reach the aims, the authors matched two types of data: co-assigned patent portfolio of four automakers and relationship type between automakers and their co-assignees. Matching the company names of the two data sources allowed the authors to obtain the final data set used to carry out extensive descriptive and regression analysis, both on a firm- and patent-level. Findings – Results show differences in the characteristics and the technological value of patented inventions in relation with the type of collaboration partner; they also support the authors in the identification of four co-patenting patterns (contingent, purposive, watchful and advanced) according to the co-patenting propensity and the presence of a preferred relationship type. Originality/value – The paper contributes to the literature by investigating the presence of differences across the patenting activities of a selection of automakers and their supplier, allied and subsidiary firms. The issue related to patent value represents an emerging area of interest in the field of collaborations for innovation. The methodology constitutes a novelty by matching two different sources and standardizing the company names (“name game”) through an automated algorithm and a double manual check, by searching company web sites and corporate trees.

VIX and the variance of Dow Jones industrial average stocks

2015 ◽  
Vol 41 (3) ◽  
pp. 226-243
Author(s):  
Andre Mollick
Keyword(s):  
Business Cycles ◽  
Stock Returns ◽  
Data Set ◽  
Content Type ◽  
Dummy Variable ◽  
Aggregate Uncertainty ◽  
Dow Jones Industrial Average ◽  
Variance Equation ◽  
The Impact ◽  
Mixture Of Distribution Hypothesis

Purpose – The purpose of this paper is to examine what happens to the variance of individual stocks forming the Dow Jones Industrial Average (DJIA) allowing for aggregate uncertainty measured by VIX, the “fear gauge index” of US options contracts. In examining each individual stock belonging to DJIA in 2011, the authors reconsider aggregate market uncertainty (VIX) as the mixing variable. In contrast to studies on the effects of VIX on the aggregate equity market, the data set used in this paper allow a further look at the proposition that market aggregate uncertainty should have varying impact on individual stock variance. Design/methodology/approach – GARCH-M models estimate individual stock returns belonging to the DJIA in 2011 on its lags and on the ARCH-M term in the mean equation linking stock returns to the variance equation. The longest time span has 5,738 observations for most stocks under daily frequency from January 3, 1990 to December 30, 2011. The authors use one lag for the VIX2 term to address simultaneity problems in the variance equation. In order to allow for interactions between volatility and business cycles, the authors include a dummy variable for the three recessions identified by the NBER over the period. Findings – Adding the “fear gauge” VIX index and a dummy variable for recessions to the variance equation in GARCH-M models, the VIX coefficient always increases variance and the recession dummy has mixed effects. Overall, VIX acts as expected as mixing variable. Supporting the mixture of distribution hypothesis, the impact of VIX is always positive (1.039 on market variance) and GARCH effects vanish completely for the index and almost as much for 24 stocks. Research limitations/implications – In theory, the effects of VIX on stock variance should be positive and statistically significant, together with reductions of GARCH persistence. The authors find this to be the case for the aggregate stock market and for 24 out of its 29 DJIA stocks. The authors leave for further work extensions to estimating the variance equation for companies very exposed to idiosyncratic changes, such as oil price fluctuations or stock buybacks. The implication of this research for the academic or financial community relies on the estimation of VIX effects on individual stock variance, controlling for business cycles. Originality/value – Due to its benchmark in equities, stocks in the Dow Jones Industrials make it a very interesting case study. This paper reconsiders the aggregate uncertainty hypothesis for two main reasons. First, the financial press and traders keep a very close track on the daily evolution of VIX. Second, recent research emphasizes the formal predictive power of VIX in US stock markets. For the variance equation, existing works report positive values for the VIX-coefficient on the S&P 500 index but they have not examined individual stocks as the authors do in this paper.

Institutional quality and trade in intermediate goods

2018 ◽  
Vol 22 (2) ◽  
pp. 162-186 ◽  
Author(s):  
Hea-Jung Hyun
Keyword(s):  
Comparative Advantage ◽  
Empirical Finding ◽  
Institutional Quality ◽  
Trade Costs ◽  
Trade Flows ◽  
Production Costs ◽  
Intermediate Goods ◽  
Omitted Variables ◽  
Data Set ◽  
Content Type

Purpose Recently published studies stress the importance of trade in intermediate goods. The literature on determinants of trade, however, have largely focused on the sources of comparative advantage in determining aggregate trade flows rather than trade in intermediate goods. The purpose of this paper is to examine the role of institutional quality and trade costs to explain the determinants of trade in intermediates. Design/methodology/approach The simple model is based on the model of comparative advantage in the gravity framework used by Eaton and Kortum (2002) and Chor (2010) to relate trade flows of intermediate goods to institutional parameters, factor endowments and geography. The empirical tests use a data set containing 172 countries and 17 industries spanning ten years. Findings The results confirm the theoretical prediction that a country with higher institutional quality has a comparative advantage in institution-intensive goods and trade costs have a negative effect on trade. The author further finds that these effects are stronger in share of trade in intermediate goods vis-à-vis final goods. Originality/value To highlight the distinct nature of trade in intermediate goods, the author separates industry trade flows as intermediate input trade and final goods (consumption goods) trade to compare the importance of different sources of comparative advantage among different types of trade flows. Unlike Eaton and Kortum (2002) and Chor (2010) who used cross-sectional data for final goods trade, the ten-year industry-level panel data are used to compare the relative importance of institutions and geography as determinants in trade in intermediate goods compared to final goods trade and capture the macroeconomic time variant factors as well as industry–country pair characteristics. A significant caveat in gravity regression is that an empirical finding may often be driven by omitted variables. Inclusion of a set of country variables such as GDP, production costs and institutional level may still allow omitted variables to bias the estimation. To avoid this problem, the author includes a fixed effect of exporter and importer as well as industry and year, instead of a set of country characteristics.

Driving forces for the US residential housing price: a predictive analysis

2019 ◽  
Vol 9 (4) ◽  
pp. 515-529
Author(s):  
Amirhosein Jafari ◽  
Reza Akhavian
Keyword(s):  
Real Estate ◽  
Predictive Models ◽  
Driving Forces ◽  
Housing Prices ◽  
Housing Price ◽  
Data Set ◽  
Content Type ◽  
The Usa ◽  
Decision Making Processes ◽  
The Impact

Purpose The purpose of this paper is to determine the key characteristics that determine housing prices in the USA. Data analytical models capable of predicting the driving forces of housing prices can be extremely useful in the built environment and real estate decision-making processes. Design/methodology/approach A data set of 13,771 houses is extracted from the 2013 American Housing Survey (AHS) data and used to develop a Hedonic Pricing Method (HPM). Besides, a data set of 22 houses in the city of San Francisco, CA is extracted from Redfin real estate brokerage database and used to test and validate the model. A correlation analysis is performed and a stepwise regression model is developed. Also, the best subsets regression model is selected to be used in HPM and a semi-log HPM is proposed to reduce the problem of heteroscedasticity. Findings Results show that the main driving force for housing transaction price in the USA is the square footage of the unit, followed by its location, and its number of bathrooms and bedrooms. The results also show that the impact of neighborhood characteristics (such as distance to open spaces and business centers) on the housing prices is not as strong as the impact of housing unit characteristics and location characteristics. Research limitations/implications An important limitation of this study is the lack of detailed housing attribute variables in the AHS data set. The accuracy of the prediction model could be increased by having a greater number of information regarding neighborhood and regional characteristics. Also, considering the macro business environment such as the inflation rate, the interest rates, the supply and demand for housing, and the unemployment rates, among others could increase the accuracy of the model. The authors hope that the presented study spurs additional research into this topic for further investigation. Practical implications The developed framework which is capable of predicting the driving forces of housing prices and predict the market values based on those factors could be useful in the built environment and real estate decision-making processes. Researchers can also build upon the developed framework to develop more sophisticated predictive models that benefit from a more diverse set of factors. Social implications Finally, predictive models of housing price can help develop user-friendly interfaces and mobile applications for home buyers to better evaluate their purchase choices. Originality/value Identification of the key driving forces that determine housing prices on real-world data from the 2013 AHS, and development of a prediction model for housing prices based on the studied data have made the presented research original and unique.

The relationship between housing prices and inflation rate in Turkey

2019 ◽  
Vol 13 (3) ◽  
pp. 427-452 ◽  
Author(s):  
Özge Korkmaz
Keyword(s):  
Price Index ◽  
Inflation Rate ◽  
Housing Prices ◽  
Consumer Price Index ◽  
Housing Price ◽  
Human Beings ◽  
Monthly Basis ◽  
Data Set ◽  
Content Type ◽  
The Relationship

Purpose Human beings need shelter as the beginning of their existence. Same holds true for people who live in Turkey as it is a cultural and traditional reason to be the host and endeavor to buy a home even if one has to pay the debt for years. Another factor that is important for individuals and even for countries is the inflation rate. In this context, the purpose of this study is to investigate whether the 26 regions of Turkey are affected by the inflationary pressure, specifically in the housing price index (HPI). Design/methodology/approach For this purpose, data from 2010:01 to 2019:01 and the consumer price index (CPI), as well as HPI have been used. The causal relationship between the variables is analyzed by Konya Causality (2006) test. Findings The key results suggest that HPI causes inflationary pressures in some regions. Research limitations/implications The study has some limitations in terms of data set and scope. These are as follows: although there are many variables affecting housing prices, this study aims to investigate the causal link between inflation and housing prices. In addition, only the CPI and HPI variables were provided on a monthly basis in the 2010-2019 period for 26 regions due to the aim of making regional propositions in the investigation of this relationship. For these reasons, different macroeconomic variables could not be studied. Originality/value This study makes the following contribution to the literature. While the majority of existing literature investigates the relationship between housing prices and inflation from an empirical perspective for country, very few studies have been for the sub-regions and also these studies have focused on only some sub-regions. In other words, in the literature review, a study has observed that Turkey has to examine the relationship between the housing price and inflation variables for all sub-regions in particular. To overcome this deficiency in the literature, this study aims to investigate the relationship between housing price and inflation for 26 regions.

Portland ordinances: tiny home and short-term rental permits

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Billie Ann Brotman
Keyword(s):  
Economic Activity ◽  
Price Index ◽  
Activity Index ◽  
Housing Price ◽  
Short Term ◽  
Content Type ◽  
Coverage Ratio ◽  
Dummy Variable ◽  
Property Owners ◽  
The City

Purpose This study aims to examine the permit changes enacted by the city of Portland, Oregon, USA, on the construction and subsequent short-term rental of tiny homes. The permitting process was eased by the city in 2014. The city’s enforcement of occupancy and rental ordinances, sometimes called Airbnb laws, were tightened in 2019. The new code restrictions are tighter than the rental codes that existed previously. Design/methodology/approach This paper uses time-series data to first consider the thesis that relaxing building permit requirements for tiny homes has encouraged legal construction and increased the number of applications filed with the city planning office. The number of permits was the dependent variable and time-sensitive dummy variable was the independent variable. An adjusted T-statistic was calculated using a least-squares regression model with a moving average autocorrelation adjustment. The second regression model considers the financial relationship between active listings on Airbnb and HomeAway to a housing price coverage ratio and the aggregated dynamic-factor model used to calculate the economic activity index for Portland. Findings There were two reported case study findings. The first regression used a dummy variable measuring the application response to permit easing. It was positive and significant. The second finding measures active host listings on Airbnb whether they are directly associated with the calculated multiple of the changes in the S&P/Case–Shiller housing price index low tier divided by weekly employee income. Higher numbers for this coverage ratio suggest that listings on short-term rental platforms are increasing directly with the ratio. The economic activity index is insignificant when predicting the level of listings. Regression results indicate that property owners are financially motivated to list dwellings as visitor rentals and possibly motivated to install tiny homes behind their primary residences as short-term rental units. Local economic conditions do not seem to influence the number of properties listed on short-term rental websites. Research limitations/implications Higher coverage ratios encourage property owners to list dwellings on short-term rental websites in the absence of enforceable rental restrictions. Without a method to quickly and feasible identify owners violating short-term rental restriction legislation and enforce fines there is a tendency for active listings to grow in a locale. San Francisco, California, under its new short-term rental ordinance requires online websites such as Airbnb to enforce permit requirements. San Francisco’s ordinance change seems to have resulted in a dramatic drop in active listings available for visitor rentals. Practical implications Information published by Inside Airbnb and Airdna does not separate entire dwelling information into categories such as single-family detached houses; tiny homes; apartments; or condominiums ownership types. Even public housing units are sometimes listed as short-term rentals. The aggregate data makes the relationship between active listings and the coverage ratio difficult to interpret. Listing information is limited and only available for a three-year rolling cycle on a quarterly basis for the city of Portland, Oregon. Social implications Future research studies could consider how tiny homes might play a role in providing permanent housing to local residents or for providing a shelter for the homeless in cities experiencing acute long-term rental shortages. Does limiting the number of homes available as short-term visitor rentals noticeably increase the quantity of housing and lower the monthly rental rates available to permanent residents of the city? Cities have passed short-term rental codes with the objective of increasing the availability of rental housing available to residents at affordable prices. Originality/value Prior research studies focused on who purchases tiny homes; tiny homes used as housing for the homeless; communities composed of tiny homes; and the connection between tiny home living and political activism. The study herein links permit changes to tiny-home building applications. It uses the home price index low tier and the economic condition index for the Portland metropolitan area to predict the number of active listings on Airbnb and HomeAway websites pre-regulation enforcement.

Financial distress determinants among SMEs: empirical evidence from Sweden

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman
Keyword(s):  
Financial Crisis ◽  
Financial Distress ◽  
Large Scale ◽  
Global Financial Crisis ◽  
Binary Logistic Regression ◽  
Data Availability ◽  
Cross Sectional ◽  
Data Set ◽  
Content Type ◽  
The Global Financial Crisis

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.

Light touch, heavy hand: principals and data-use PLCs

2017 ◽  
Vol 55 (4) ◽  
pp. 376-389 ◽  
Author(s):  
Alice Huguet ◽  
Caitlin C. Farrell ◽  
Julie A. Marsh
Keyword(s):  
Professional Learning ◽  
Low Income ◽  
Equal Opportunity ◽  
Data Use ◽  
Opportunity To Learn ◽  
School Level ◽  
Comparative Case Study ◽  
Data Set ◽  
Content Type ◽  
Principal's Role

Purpose The use of data for instructional improvement is prevalent in today’s educational landscape, yet policies calling for data use may result in significant variation at the school level. The purpose of this paper is to focus on tools and routines as mechanisms of principal influence on data-use professional learning communities (PLCs). Design/methodology/approach Data were collected through a comparative case study of two low-income, low-performing schools in one district. The data set included interview and focus group transcripts, observation field notes and documents, and was iteratively coded. Findings The two principals in the study employed tools and routines differently to influence ways that teachers interacted with data in their PLCs. Teachers who were given leeway to co-construct data-use tools found them to be more beneficial to their work. Findings also suggest that teachers’ data use may benefit from more flexibility in their day-to-day PLC routines. Research limitations/implications Closer examination of how tools are designed and time is spent in data-use PLCs may help the authors further understand the influence of the principal’s role. Originality/value Previous research has demonstrated that data use can improve teacher instruction, yet the varied implementation of data-use PLCs in this district illustrates that not all students have an equal opportunity to learn from teachers who meaningfully engage with data.

A dynamic simulator for the ergonomics evaluation of powered torque tools for human assembly

2017 ◽  
Vol 37 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Haluk Ay ◽  
Anthony Luscher ◽  
Carolyn Sommerich
Keyword(s):  
Risk Factors ◽  
Joint Torque ◽  
Human Hand ◽  
Model Parameters ◽  
Test Rig ◽  
Musculoskeletal Discomfort ◽  
Data Set ◽  
Content Type ◽  
Working Posture ◽  
Displacement Measurements

Purpose The purpose of this study is to design and develop a testing device to simulate interaction between human hand–arm dynamics, right-angle (RA) computer-controlled power torque tools and joint-tightening task-related variables. Design/methodology/approach The testing rig can simulate a variety of tools, tasks and operator conditions. The device includes custom data-acquisition electronics and graphical user interface-based software. The simulation of the human hand–arm dynamics is based on the rig’s four-bar mechanism-based design and mechanical components that provide adjustable stiffness (via pneumatic cylinder) and mass (via plates) and non-adjustable damping. The stiffness and mass values used are based on an experimentally validated hand–arm model that includes a database of model parameters. This database is with respect to gender and working posture, corresponding to experienced tool operators from a prior study. Findings The rig measures tool handle force and displacement responses simultaneously. Peak force and displacement coefficients of determination (R2) between rig estimations and human testing measurements were 0.98 and 0.85, respectively, for the same set of tools, tasks and operator conditions. The rig also provides predicted tool operator acceptability ratings, using a data set from a prior study of discomfort in experienced operators during torque tool use. Research limitations/implications Deviations from linearity may influence handle force and displacement measurements. Stiction (Coulomb friction) in the overall rig, as well as in the air cylinder piston, is neglected. The rig’s mechanical damping is not adjustable, despite the fact that human hand–arm damping varies with respect to gender and working posture. Deviations from these assumptions may affect the correlation of the handle force and displacement measurements with those of human testing for the same tool, task and operator conditions. Practical implications This test rig will allow the rapid assessment of the ergonomic performance of DC torque tools, saving considerable time in lineside applications and reducing the risk of worker injury. DC torque tools are an extremely effective way of increasing production rate and improving torque accuracy. Being a complex dynamic system, however, the performance of DC torque tools varies in each application. Changes in worker mass, damping and stiffness, as well as joint stiffness and tool program, make each application unique. This test rig models all of these factors and allows quick assessment. Social implications The use of this tool test rig will help to identify and understand risk factors that contribute to musculoskeletal disorders (MSDs) associated with the use of torque tools. Tool operators are subjected to large impulsive handle reaction forces, as joint torque builds up while tightening a fastener. Repeated exposure to such forces is associated with muscle soreness, fatigue and physical stress which are also risk factors for upper extremity injuries (MSDs; e.g. tendinosis, myofascial pain). Eccentric exercise exertions are known to cause damage to muscle tissue in untrained individuals and affect subsequent performance. Originality/value The rig provides a novel means for quantitative, repeatable dynamic evaluation of RA powered torque tools and objective selection of tightening programs. Compared to current static tool assessment methods, dynamic testing provides a more realistic tool assessment relative to the tool operator’s experience. This may lead to improvements in tool or controller design and reduction in associated musculoskeletal discomfort in operators.

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