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This study aims to examine the effect of proximity and spatial dependence on the house price index for the nascent market Dar es Salaam, Tanzania. Despite the ongoing housing market transactions, there is no single house price index that takes into account proximity and spatial dependence. The proximity considerations in question are proximal to arterial roads, public hospitals, an airport and food markets. Previous studies on sub-Saharan Africa have focused on the ordinary least squares (OLS)-based hedonic model for the index and ignored spatial and proximity considerations.

Using the OLS and spatial econometric approach, the paper tests for the significance of the two effects – proximity and spatial dependence in the hedonic price model with year dummy variables from 2010 to 2019. The paper then compares the three indices in the following configurations: without the two effects, with proximity factors only, and with both effects, i.e. proximity and spatial dependence.

The inclusion of proximity factors and spatial dependence – spatial autocorrelation – seems to improve the hedonic price model but does not significantly improve the house price index. However, further research should be called for on account of the nascent nature of the market.

The paper brings new knowledge by demonstrating that it may not be necessary to take into account proximity factors and spatial dependence for the Dar es Salaam house price index.

The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.

The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al. (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model.

The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy.

The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation.

Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

The purpose of this study is to quantify and evaluate the efficiency of knowledge services public libraries (PLKS) in China and to analyze the factors affecting the efficiency of PLKS. The focus of this study is to explore the characteristics and changing trends of PLKS and how to improve the efficiency of PLKS. The conclusions drawn in this paper are expected to provide important references for academic research and practical exploration in related fields.

This study constructs a three-stage process of PLKS. On this basis, the efficiency is analyzed by using the Super-NSBM model, and the influencing factors of PLKS are analyzed by using OLS. Finally, through the comprehensive interpretation of the measured data and the existing social development conditions, the authors put forward relevant recommendations.

In the first stage, the PLKS in China meets the basic requirements, but there is an obvious waste of resources in the second stage. In addition, China’s public libraries show obvious regional differences in knowledge service efficiency. Finally, it was found that economic and technological factors can increase the efficiency of PLKS, but the increase in the number of university students will reduce the efficiency of PLKS.

In this study, the authors provide an exhaustive deconstruction and explanation of PLKS, which leads to the proposal of a three-stage PLKS efficiency conversion process. Furthermore, the authors identify a diverse set of quantifiable metrics that are readily accessible for open collection. Notably, the authors correlated the efficiency of PLKS with the influencing factors, exploring PLKS from a new perspective and enriching the research related to PLKS.

We model the conditions under which parents optimally reallocate time to childcare when an outside agent exogenously restricts the number of hours an employer can demand of a working parent. Theoretically, when the restriction binds, a parent's available time increases. We exploit a series of voluntary and mandated labor-market reforms in South Korea that regulated the statutory and maximum work hours of parents. The government implemented the laws in stages by industry and size of firms. This implementation process generates exogenous variation across families where one or both partners worked at jobs that were or were not affected by the reform. We show the reforms affected work hours and use the predicted changes to investigate the total amount they spent on paid childcare and whether or not they changed the relative use of market and parental care. When fathers get more time (work less), parents spend less money on childcare. A change in mother's work time does not affect expenditures. When parents get more time, they are more likely to spend money on paid childcare for school-age children and more likely to use private academies.

This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities.

This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal.

The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation.

The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation.

This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study.

This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets.

Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

This study investigates the association between corporate sustainability disclosures and firm performance and value.

This study collected data from 694 manufacturing companies operating in 34 countries between 2007 and 2019, yielding 6,181 firm-year observations. This study employs a dual-model framework to analyze the influence of environmental, social, and governance (ESG) performance on return on assets (ROA), return on equity (ROE), and Tobin's Q ratio. Two sets of control variables, firm- and country-specific, were incorporated to account for potential confounding factors. To validate the robustness of the findings, we utilized a battery of econometric techniques, including traditional ordinary least squares (OLS), firm-fixed effects, quantile regression, and instrumental variables-generalized method of moments (IV-GMM), applied to both the pooled and firm-fixed effects models.

The findings are contradictory: there is a negative relationship between sustainability disclosure and operating performance and return on equity, but a positive relationship between sustainability disclosure and firm value. The negative correlation is consistent with agency theory and the positive correlation is consistent with the legitimacy and shareholder theories. These results are robust to performance measures and estimation methods.

Short-term profit shouldn't deter sustainability. It boosts legitimacy, reputation, efficiency, and long-term market value. Investors must look beyond profitability ratios, embracing ESG metrics. Firms should see sustainability as strategic investment, not cost. Patience pays off: long-term gains await. Regulation can guide balanced growth, prioritizing both shareholders and societal well-being.

This study is the first to adopt a firm’s fixed-effect quantile regression, which provides deep insights into the role of sustainability disclosure in meeting stakeholders’ expectations.

The purpose of this study is to examine whether PV uptake is associated with key housing market determinants and linked to socio-economic profiles. An abundance of extant literature has examined the role of solar photovoltaic (PV) adoption and user costs, with an emerging corpus of literature investigating the role of the determinants of PV uptake, particularly in relation to the built environment and the spatial variation of PV dependency and dissimilarity. Despite this burgeoning literature, there remains limited insights from the UK perspective on housing market characteristics driving PV adoption and in relation spatial differences and heterogeneity that may exist.

Applying micro-based data at the Super Output Area-level geography, this study develops a series of ordinary least squares, spatial econometric models and a logistic regression analysis to examine built environment, housing tenure and deprivation attributes on PV adoption at the regional level in Northern Ireland, UK.

The findings emerging from the research reveal the presence of some spatial clustering and PV diffusion, in line with several existing studies. The findings demonstrate that an urban-rural dichotomy exists seemingly driven by social interaction and peer effects which has a profound impact on the likelihood of PV adoption. Further, the results exhibit tenure composition and “economic status” to be significant and important determinants of PV diffusion and uptake.

Housing market characteristics such as tenure composition across local market structures remain under-researched in relation to renewable energy uptake and adoption. This study examines the role of housing market attributes relative to socio-economic standing for adopting renewable energy.

This paper explores if the firm’s strategic orientation can be associated with differences in cost behavior activity.

Using Compustat data from 1979 to 2012, the archival study examines if there are differences between differentiation and cost leadership strategies on the firm’s cost stickiness.

The main finding provides evidence that firms pursuing a differentiation strategy exhibit greater cost stickiness, on average, as compared to firms pursuing a cost leadership strategy. This relationship is moderated by the optimistic or pessimistic expectations of managers for future sales.

This paper contributes to the literature on cost management by explaining how strategic positioning affects firms’ cost behavior using the framework of asymmetric cost behavior. The study encompasses both product and period costs and documents the impact on earnings.