scholarly journals Housing supply elasticity and growth: evidence from Italian cities

2020 ◽  
Author(s):  
A Accetturo* ◽  
A R Lamorgese** ◽  
S Mocetti ◽  
D Pellegrino**
Keyword(s):  
Housing Stock ◽  
House Prices ◽  
Labor Demand ◽  
Housing Supply ◽  
Physical Constraints ◽  
Supply Elasticity ◽  
Housing Supply Elasticity ◽  
Using Data ◽  
The Impact ◽  
The City

Abstract This paper examines the impact of housing supply elasticity on urban development. Using data for a sample of roughly one hundred Italian main cities observed over 40 years, we first estimate housing supply elasticities at the city level, measured as the correlation between the changes in the housing stock and in the house prices. Second, we show that differences in the elasticity of housing supply may determine the extent to which a demand shock translates into more intense employment growth or more expensive houses. To address endogeneity of housing supply elasticity, we exploit a synthetic measure of physical constraints to residential development as instrumental variable. We find that an exogenous increase in labor demand determines a rise of employment and house prices; however, in cities with a less elastic housing supply, the impact on economic growth is significantly lessened while the effects on house prices are larger.

scholarly journals Relationship between Changes over Time in Factors, Including the Impact of Meteorology on Photochemical Oxidant Concentration and Causative Atmospheric Pollutants in Kawasaki

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 446
Author(s):  
Akinori Fukunaga ◽  
Takaharu Sato ◽  
Kazuki Fujita ◽  
Daisuke Yamada ◽  
Shinya Ishida ◽  
...  
Keyword(s):  
Moving Average ◽  
Atmospheric Pollutants ◽  
Important Indicator ◽  
Photochemical Oxidant ◽  
Oxidant Concentration ◽  
Using Data ◽  
Changes Over Time ◽  
The Impact ◽  
The Relationship ◽  
The City

To clarify the relationship between changes in photochemical oxidants’ (Ox) concentrations and their precursors in Kawasaki, a series of analyses were conducted using data on Ox, their precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), and meteorology that had been monitored throughout the city of Kawasaki for 30 years from 1990 to 2019. The trend in air temperature was upward, wind speed was downward, and solar radiation was upward, indicating an increasing trend in meteorological factors in which Ox concentrations tend to be higher. Between 1990 and 2013, the annual average Ox increased throughout Kawasaki and remained flat after that. The three-year moving average of the daily peak increased until 2015, and after that, it exhibited a slight decline. The amount of generated Ox is another important indicator. To evaluate this, a new indicator, the daytime production of photochemical oxidant (DPOx), was proposed. DPOx is defined by daytime averaged Ox concentrations less the previous day’s nighttime averaged Ox concentrations. The trend in DPOx from April to October has been decreasing since around 2006, and it was found that this indicator reflects the impact of reducing emissions of NOx and VOCs in Kawasaki.

Quantifying changes in bicycle volumes using crowdsourced data

2022 ◽  
pp. 239980832110661
Author(s):  
Ali Al-Ramini ◽  
Mohammad A Takallou ◽  
Daniel P Piatkowski ◽  
Fadi Alsaleem
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Crowdsourced Data ◽  
Machine Learning Approach ◽  
Bicycle Infrastructure ◽  
The Difference ◽  
Infrastructure Investments ◽  
Using Data ◽  
The Impact ◽  
The City

Most cities in the United States lack comprehensive or connected bicycle infrastructure; therefore, inexpensive and easy-to-implement solutions for connecting existing bicycle infrastructure are increasingly being employed. Signage is one of the promising solutions. However, the necessary data for evaluating its effect on cycling ridership is lacking. To overcome this challenge, this study tests the potential of using readily-available crowdsourced data in concert with machine-learning methods to provide insight into signage intervention effectiveness. We do this by assessing a natural experiment to identify the potential effects of adding or replacing signage within existing bicycle infrastructure in 2019 in the city of Omaha, Nebraska. Specifically, we first visually compare cycling traffic changes in 2019 to those from the previous two years (2017–2018) using data extracted from the Strava fitness app. Then, we use a new three-step machine-learning approach to quantify the impact of signage while controlling for weather, demographics, and street characteristics. The steps are as follows: Step 1 (modeling and validation) build and train a model from the available 2017 crowdsourced data (i.e., Strava, Census, and weather) that accurately predicts the cycling traffic data for any street within the study area in 2018; Step 2 (prediction) use the model from Step 1 to predict bicycle traffic in 2019 while assuming new signage was not added; Step 3 (impact evaluation) use the difference in prediction from actual traffic in 2019 as evidence of the likely impact of signage. While our work does not demonstrate causality, it does demonstrate an inexpensive method, using readily-available data, to identify changing trends in bicycling over the same time that new infrastructure investments are being added.

scholarly journals Building Stability On Permafrost In Vorkuta, Russia

2019 ◽  
Author(s):  
P. I. Kotov ◽  
V. Z. Khilimonyuk
Keyword(s):  
Climate Change ◽  
Housing Stock ◽  
Residential Buildings ◽  
Open Data ◽  
Urban Environments ◽  
The Arctic ◽  
Residential Housing ◽  
Infrastructure Stability ◽  
The Impact ◽  
The City

The Infrastructure stability on permafrost is currently an important topic as the Arctic countries are developing climate change adaptation and mitigation programs. Assessing the sustainability of infrastructure facilities (especially in urban environments) is a difficult task as it depends on many parameters. This article discusses the city of Vorkuta, which is located in the northwest of Russia. This city differs from many others built on permafrost because most of buildings were built according to Principle II (The Active Method) of construction on permafrost with thawing soil prior to construction. Assessments of the engineering and geocryological conditions, basic principles of construction in the city, and reasons for building failures, were carried out within this study. The research is based on publications, open data about buildings, and visual observations in Vorkuta. About 800 buildings are in use in Vorkuta in 2020 (43% of what it was 50 years ago). According to the analysis, about 800 houses have been demolished or disconnected from utility lines over the past 50 years (about 250 of these are still standing, pending demolition). Since 1994, the construction of new residential buildings has almost stopped. Therefore, buildings that have been in use for over 50 years will account for 90% of the total residential housing stock by 2040. The effects of climate change in the city will depend primarily on the principle of construction employed and on the geocryological conditions of the district. Buildings constructed according to Principle I (The Passive Method) were found to be more vulnerable due to a decrease in permafrost bearing capacity. The impact of increasing air temperature on some of the buildings built on bedrock (the central part of the city) and some built on thawing soil will be minimal, as other factors are more significant.

scholarly journals Housing Supply in the Presence of Informality

2021 ◽  
Author(s):  
Ricardo Guedes ◽  
Felipe Iachan ◽  
Marcelo Sant'Anna
Keyword(s):  
Metropolitan Areas ◽  
Demographic Data ◽  
Housing Prices ◽  
Housing Supply ◽  
Informal Housing ◽  
Supply Elasticity ◽  
Downward Pressure ◽  
Housing Supply Elasticity ◽  
City Model ◽  
Identification Strategy

We study housing supply in markets where informal housing is common. Using a combination of census and satellite data, we estimate housing supply for more than 90 metropolitan areas in Brazil. We find that widespread informal housing increases the housing supply elasticity, partially offsetting the downward pressure of geographical constraints. Our empirical approach is guided by a monocentric city model that includes informal housing. Our identification strategy relies on the use of two novel instruments, combining demographic data and public land ownership. We use the supply elasticity estimates to forecast the response of future housing prices to natural population growth.

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