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This study aims to analyze the impact of COVID-19 on housing price within four major metropolitan areas in Texas: Austin, Dallas, Houston and San Antonio. The analysis intends to understand economic and mobility drivers behind the housing market under the inclusion of fixed and random effects.

This study used a linear mixed effects model to assess the socioeconomic and housing and transport-related factors contributing to median home prices in four major cities in Texas and to capture unobserved factors operating at spatial and temporal level during the COVID-19 pandemic.

The regression results indicated that an increase in new COVID-19 cases resulted in an increase in housing price. Additionally, housing price had a significant and negative relationship with the following variables: business cycle index, mortgage rate, percent of single-family homes, population density and foot traffic. Interestingly, unemployment claims did not have a significant impact on housing price, contrary to previous COVID-19 housing market related literature.

Previous literature analyzed the housing market within the first phase of COVID-19, whereas this study analyzed the effects of the COVID-19 throughout the entirety of 2020. The mixed model includes spatial and temporal analyses as well as provides insight into how quantitative-based mobility behavior impacted housing price, rather than relying on qualitative indicators such as shutdown order implementation.

The purpose of this paper is to ascertain how elements of the built environment may or may not influence the frequency of grocery shopping.

Using data from the 2009 Seattle Obesity Study, the research investigated the effect of the urban built environment on grocery shopping travel frequency in the Seattle-King County area. Binary and ordered logit models served to estimate the impact of individual characteristics and built environments on grocery shopping travel frequency.

The results showed that the respondents’ attitude towards food, travel mode, and the network distance between homes and stores exerted the strongest influence on the travel frequency while urban form variables only had a modest influence. The study showed that frequent shoppers were more likely to use alternative transportation modes and shopped closer to their homes and infrequent shoppers tended to drive longer distances to their stores and spent more time and money per visit.

This research has implications for urban planners and policy makers as well as grocery retailers, as the seemingly disparate groups both have an interest in food shopping frequency.

Few studies in the planning or retail literature investigate the influence of the urban built environment and the insights from the planning field. This study uses GIS and a planning framework to provide information that is relevant for grocery retailers and those invested in food distribution.

This study aims to analyze the impact of technology-based corporation relocation on housing price indices during COVID-19 within the metropolitan areas of Austin, Texas and Seattle/Bellevue, Washington.The corporations under observation were Tesla and Amazon, respectively. The analysis intends to understand economic drivers behind the housing market and the radius of its effect while including fixed and random effects.

This study used a difference-in-difference (DID) method to evaluate changes in housing price index near and further away from Tesla’s and Amazon’s new corporate locations. The DID method allows for the capture of unique regional characteristics, as it requires a treatment and control group: housing price index and 5-mile and 10-mile search radii centered from the new corporate location.

The results indicated that corporate relocation announcements had a positive effect on housing price index post-pandemic. Specifically, the effect of Tesla’s relocation in Austin on the housing price index was not concentrated near the relocation site, but beyond the 5- and 10-mile radii. For Seattle/Bellevue, the effect of Amazon’s relocation announcement on housing price index was concentrated near the relocation site as well as beyond a 10-mile radius. Interestingly, these findings suggest housing markets incorporate speculation of prospective economic expansion linked with a corporate relocation.

Previous literature assessed COVID-19 housing market conditions and the economic effects of corporate relocation separately, whereas this study analyzed the housing price effects of corporate relocation during COVID-19. The DID method includes spatial and temporal analyses that allow for the impact of housing price to be observed across specified radii rather than a city-wide impact analysis.

By comparing regression models, this study aims to analyze the added home value of green sustainability features and green efficiency characteristics, rather than green certifications, in the city of Austin.

The adoption of home green energy efficiency upgrades has emerged as a new trend in the real estate industry, offering several benefits to builders and home buyers. These include tax reductions, health improvements and energy savings. Previous studies have shown that energy-certified single-family homes command a premium in the marketplace. However, the literature is limited in its analysis of the effects of green upgrades and certification on different types of single-family homes. To address this gap, this research collected data from 21,292 multiple listing services (MLS) closed home-selling listings in Austin, Texas, over a period of 35 months.

The analysis results showed that green efficiency features could generally increase single-family housing prices by 11.9%, whereas green sustainability upgrades can potentially bring a 11.7% higher selling price. Although green housing certification did not have significant effects on most housing groups, it did increase closing prices by 13.2% for single-family residences sold at the medium price range, which is higher than the impacts from simply listing the green features on MLS.

The study contributes to the body of knowledge by examining the market value of broadly defined energy efficiency and sustainability features in the residential housing market. The findings can help policymakers, brokerage firms, home builders and owners adjust their policies and strategies related to single-family home sales and mortgage approvals. The research also highlights the potential benefits of capitalizing on green housing features other than certifications.

This study aims to explore the impacts of Airbnb listings on land values in the Austin, Texas, USA area, particularly on single-family homes. The goal of the analysis is to shed light on how greatly and in what direction Airbnb is affecting the housing market, with an emphasis on the spatial distribution of its effects.

The analysis in this paper is performed using three distinct models on a data set of land parcel data within Travis County: an ordinary least squares regression model, a geographically weighted regression (GWR) aimed at detecting the influence of variables at the census tract level, and a Bayesian approach, which describes spatial and temporal effects on the data.

The findings of the analysis indicate that across the years 2013 to 2019, higher numbers of Airbnb listings were associated with lower percentage increases in land value in certain tracts in the northern and eastern parts of the city. Additionally, the results of the Bayesian model indicated that much of the change in land value can be attributed to unobserved factors within census tracts.

The contribution of this study to the existing literature is its analysis of the spatial and temporal analysis of the effects of Airbnb listings on land value using a GWR and a Bayesian model. Also, as the negative correlation found in the study departs from previous research, this paper may provide policymakers insight into the complex spatial distribution and conflicting effects of Airbnb listings across distinct parts of cities.

The purpose of this paper is to propose an alternative test board design with only one loading condition and sufficiently large sample size, which is more suitable for the statistical package qualification. With the exception of the board shape and size and package component layout, all other aspects of the design strictly follow the JEDEC standard so that the board design can be easily implemented.

A test board in a round shape was introduced. First, drop tests were carried out. Then, the dye stain test and metallurgical analysis were performed in order to study the failure mechanism of lead‐free solder joint under drop impact.

The test results indicate that the combined effect of mechanical shock and PCB bending vibration is the root cause of solder joint failure under drop impact, and that the maximum peeling stress of the critical solder joint could be considered to be the dominant failure factor. On the other hand, the fracture of BGA lead‐free solder joints occurs at intermetallic compound (IMC) interface near the package side, and failure mode is brittle fracture.

These results are the same as those of JEDEC standard test board. Furthermore, the solder joint loading conditions in this design are simplified from six to one. The round test board can take the place of JEDEC standard test board to carry out drop test and to enable good solder joint life prediction and statistical analysis.

This paper aims to investigate the influences of dimple location on the heat transfer performance of a pin fin-dimpled channel with upright/curved/inclined pin fins under stationary and rotating conditions.

Numerical methods based on a realizable k-ε turbulent model are used to conduct this study. Three kinds of pin fins (upright, curved, inclined) and three dimple locations (front, middle, behind) are studied for Ro varying from 0 to 0.5.

On the whole, pin fin plays a dominated role in heat transfer performance compared to dimple. The heading path and interaction of the longitudinal secondary flow and jet-like flow critically affect heat transfer performance. The formation, development and impingement of jet-like flow and longitudinal secondary flow are significantly affected by dimple locations. Dimple at behind position shows the poorest heat transfer enhancement.

This study is an extend of another previous study in which an innovative curved pin fin is proposed. The originality of this paper is to evaluate the heat transfer performance for the combined cooling structure of dimple and pin fin, which will provide original and useful application and experience for turbine blade design.