Revisiting metropolitan house price-income relationships

Oikarinen, Elias; Bourassa, Steven C.; Hoesli, Martin; Engblom, Janne

doi:10.2139/ssrn.3830512

Cited by 4 publications

(4 citation statements)

References 52 publications

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“…However, economic development is not solely represented by the building area, as the size of a house does not always signify the economic status of the homeowner. Economic development is contingent upon other variables, they are population density [23][24][25][26][27], area characteristics, whether the region is urban or rural [28][29][30], and Gross Domestic Product (GDP) in the area [31][32][33]. Therefore, population density, area characteristics (urban or rural), and GDP represent the variables of interest in this research.…”

Section: Determination Of Variablesmentioning

confidence: 99%

Evaluation of formal waste reduction facility location compared to recyclable plastic waste generation in Denpasar City, Bali, Indonesia

Agustina,

Sembiring,

Sakti

2024

E3S Web of Conf.

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This paper aims to evaluate the location of formal waste reduction facilities in comparison to the distribution of recyclable plastic waste generation in Denpasar City, Bali Province, Indonesia. The distribution of recyclable plastic waste generation was carried out by conducting primary sampling from 200 houses, following the guidelines of SNI-19-3964-1994. Socioeconomic variables, including house size, population density, Gross Domestic Product (GDP), and area classification, were obtained through interviews and the use of remote sensing data products. The distribution of recyclable plastic waste is modeled using the best of six machine learning models: LGBM (Light Gradient Boosting Machine), Linear Regression, Random Forest, and SVM (Support Vector Machine), XGBoost, and Adaboost. The LGBM model was selected with an R2 of 0.939 in the training dataset, an R2 of 0.954 in the testing dataset, and the lowest RMSE and MAE. The map of recyclable plastic waste generation distribution is created through a spatial analysis that consists of three classes with ranges of <248.5, >248.5 and <732.5, and >732.5 grams/household/week. The effectiveness of the coverage area and capacity through spatial analysis indicates that the waste reduction facilities in Denpasar City are 32% and 46%, respectively.

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Section: Determination Of Variablesmentioning

confidence: 99%

Evaluation of formal waste reduction facility location compared to recyclable plastic waste generation in Denpasar City, Bali, Indonesia

Agustina,

Sembiring,

Sakti

2024

E3S Web of Conf.

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“…Masalah peningkatan harga rumah turut berlaku di negara lain seperti Amerika Syarikat. Nisbah harga rumah kepada pendapatan di negara ini juga tidak menentu walaupun dalam jangka panjang merentasi 70 kawasan metropolitan (Oikarinen et al, 2021). Hubungan antara harga rumah dan pendapatan dijangka mempengaruhi penggunaan isi rumah, simpanan dan ketidaksamaan penggunaan (Oikarinen et al, 2021).…”

Section: Isu Pemilikan Rumah Dalam Kalangan M40 Bandarunclassified

“…Nisbah harga rumah kepada pendapatan di negara ini juga tidak menentu walaupun dalam jangka panjang merentasi 70 kawasan metropolitan (Oikarinen et al, 2021). Hubungan antara harga rumah dan pendapatan dijangka mempengaruhi penggunaan isi rumah, simpanan dan ketidaksamaan penggunaan (Oikarinen et al, 2021). Keadaan ini dapat dilihat di Pulau Pinang apabila berlakunya isu penawaran rumah mewah lebih tinggi daripada permintaan, manakala penawaran rumah mampu milik tidak mencukupi berbanding permintaan.…”

Section: Isu Pemilikan Rumah Dalam Kalangan M40 Bandarunclassified

Faktor- Faktor yang Mempengaruhi Kemampuan Pemilikan Rumah dalam kalangan Isi Rumah Bandar M40 di Pulau Pinang

Azahar

Malek

2023

MJSSH

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Pemilikan rumah merupakan elemen penting dalam memastikan keperluan setiap individu dan keluarga terhadap tempat tinggal dipenuhi untuk menjamin kesejahteraan mereka. Namun, kemampuan untuk memiliki rumah dalam kalangan isi rumah bandar M40 di Pulau Pinang adalah sederhana di mana masih terdapat segelintir yang tidak mampu memiliki rumah sendiri. Oleh itu, kajian ini bertujuan untuk mengkaji faktor-faktor yang mempengaruhi kemampuan memiliki rumah dalam kalangan isi rumah bandar M40 di Pulau Pinang. Kajian ini menggunakan pendekatan kualitatif di mana pengkaji menggunakan instrumen temu bual separa berstruktur. Seramai 20 orang ketua isi rumah M40 bandar telah ditemu bual. Kaedah analisis tematik digunakan untuk menganalisis data kualitatif bagi mencapai objektif kajian. Melalui kajian ini, cabaran sebenar yang dihadapi oleh isi rumah bandar M40 dapat dikenal pasti. Hasil kajian menunjukkan bahawa kos perumahan dan kriteria rumah merupakan faktor utama yang mempengaruhi kemampuan untuk memiliki rumah dalam kalangan isi rumah bandar M40 di Pulau Pinang. Selain itu terdapat juga beberapa faktor mempengaruhi lain seperti pendapatan, simpanan dan pinjaman perumahan. Hasil kajian ini diharap dapat membantu Kerajaan Persekutuan dan Kerajaan Negeri dalam menggubal dasar perumahan khususnya dasar rumah mampu milik yang lebih komprehensif pada masa hadapan.

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“…Bhatt and Kishor highlighted that excessive credit building had a detrimental influence on the growth of home prices [7]. Similarly, Oikarinen et al underscored that even over the long term, regional housing price-income ratios were frequently unstable and the significant geographical variances and the elasticity of the housing market are strongly connected [8]. Li et al further deduced that based on the supply-demand theory, alterations in rental housing supply exert an influence on house prices [9].…”

Section: Introductionmentioning

confidence: 99%

Influence Factor Analysis and Forecast of US House Prices Based on Linear Regression and Time Series

2023

AEMPS

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In the United States, house prices remain a perennial topic of interest for every resident. Beyond influencing individual housing choices, fluctuations in house prices play a pivotal role in shaping pleasant lifestyles and ensuring the nation's stable economic growth. The housing market, in fact, underpins the entire U.S. economic system. This study employed a multifaceted approach, utilizing both multiple linear regression models and time series analysis, to delve deeply into the factors influencing U.S. house prices. The research identified that residential construction spending and GDP are the most significant drivers leading to an increase in the American House Price Index (HPI). Additionally, through time series analysis, various graphical representations were constructed to visualize the quarterly trends in the American HPI, rendering the findings more comprehensible. In essence, this research offers a scientific foundation for future adjustments in housing price policies. It holds substantial relevance for all stakeholders in the real estate market, from buyers and sellers to developers and policymakers.

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Revisiting metropolitan house price-income relationships

Cited by 4 publications

References 52 publications

Evaluation of formal waste reduction facility location compared to recyclable plastic waste generation in Denpasar City, Bali, Indonesia

Evaluation of formal waste reduction facility location compared to recyclable plastic waste generation in Denpasar City, Bali, Indonesia

Faktor- Faktor yang Mempengaruhi Kemampuan Pemilikan Rumah dalam kalangan Isi Rumah Bandar M40 di Pulau Pinang

Influence Factor Analysis and Forecast of US House Prices Based on Linear Regression and Time Series

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