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Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

Demand forecasts are a key component of planning efforts and are crucial for managing core operations. This study aims to evaluate the use of several machine learning (ML) algorithms to forecast demand for residential construction in Jordan.

The identification and selection of variables and ML algorithms that are related to the demand for residential construction are indicated using a literature review. Feature selection was done by using a stepwise backward elimination. The developed algorithm’s accuracy has been demonstrated by comparing the ML predictions with real residual values and compared based on the coefficient of determination.

Nine economic indicators were selected to develop the demand models. Elastic-Net showed the highest accuracy of (0.838) versus artificial neural networkwith an accuracy of (0.727), followed by Eureqa with an accuracy of (0.715) and the Extra Trees with an accuracy of (0.703). According to the results of the best-performing model forecast, Jordan’s 2023 first-quarter demand for residential construction is anticipated to rise by 11.5% from the same quarter of the year 2022.

The results of this study extend to the existing body of knowledge through the identification of the most influential variables in the Jordanian residential construction industry. In addition, the models developed will enable users in the fields of construction engineering to make reliable demand forecasts while also assisting in effective financial decision-making.

Several studies have examined the impact of market fundamentals on house prices. However, the effect of economic sectors on housing prices is limited despite the existence of two-speed economies in some countries, such as South Africa. Therefore, this study aims to examine the impact of mining activities on house prices. This intends to understand the direction of house price spreads and their duration so policymakers can provide remediation to the housing market disturbance swiftly.

This study investigated the effect of mining activities on house prices in South Africa, using quarterly data from 2000Q1 to 2019Q1 and deploying an auto-regressive distributed lag model.

In the short run, we found that changes in mining activities, as measured by the contribution of this sector to gross domestic product, impact the housing price of mining towns directly after the first quarter and after the second quarter in the non-mining cities. Second, we found that inflationary pressure is instantaneous and impacts house prices in mining towns only in the short run but not in the long run, while increasing housing supply will help cushion house prices in both submarkets. This study extended the analysis by examining a possible spillover in house prices between mining and non-mining towns. This study found evidence of spillover in housing prices from mining towns to non-mining towns without any reciprocity. In the long run, a mortgage lending rate and housing supply are significant, while all the explanatory variables in the non-mining towns are insignificant.

These results reveal that enhanced mining activities will increase housing prices in mining towns after the first quarter, which is expected to spill over to non-mining towns in the next quarter. These findings will inform housing policymakers about stabilising the housing market in mining and non-mining towns. To the best of the authors’ knowledge, this study is the first to measure the contribution of mining to house price spillover.

This study aims to gain insight into the motivations behind the decision to use high-cost payday loans by households who possess mainstream credit and to determine whether this behavior has changed over time.

Using data from Statistics Canada’s Surveys of Financial Security, probit models are used to examine the sociodemographic and financial indicators associated with payday loan use.

The analysis uncovers the sociodemographic and financial characteristics of payday loan-user households with access to lower-cost short-term loans. The findings indicate that the likelihood of payday loan use has risen over time. Additional analysis reveals that indicators of financial instability are positively associated with payday loan use among this group.

This research highlights the dichotomy of payday loan users and recommends policymakers tailor solutions to the specific needs of different types of payday loan users.

This research highlights the distinguishing sociodemographic and financial characteristics of payday loan user households and recommends policymakers tailor solutions to the specific needs of different types of payday loan users.

This is the first study, to our knowledge, to focus analysis on payday loan use of those with access to lower-cost short-term credit alternatives in Canada and to include measures of financial instability in the analysis. This research is timely given the current economic environment of high interest rates and high levels of household debt.

Sustainability represents innovative elements in determining the profitability of real estate investments, among other factors, including the green component in real estate. Evidence from the literature has pointed out that incorporating green features into residential buildings can reduce operational costs and increase the building’s value. Although green real estate is considered the future trend of choice, it is still being determined whether prospective buyers are willing to accept the extra cost of green residential investment. Therefore, this study aims to investigate the effect of housing attributes and green certification on residential real estate prices.

The impact of the housing attribute and green certification in the residential sectors was assessed using a transaction data set comprising approximately 861 residential units sold in Selangor, Malaysia, between 2014 and 2022. Linear and quantile regression were used in this study by using SPSS software for a robust result.

The findings indicate that the market price of residential properties in Malaysia is influenced by housing attributes, transaction types and Green Building Index certification. The empirical evidence from this study suggests that green certification significantly affects the sales price of residential properties in Malaysia. The findings of this research will help investors identify measurable factors that affect the transaction prices of green-certified residential real estate. These identifications will facilitate the development of strategic plans aimed at achieving sustainable rates of return in the sustainable residential real estate market.

Specifically, this research will contribute to achieving area 4 of the 11th Malaysia Plan, which pertains to pursuing green growth for sustainability and resilience. This will be achieved by enhancing awareness among investors and homebuyers regarding the importance of green residential buildings in contributing to the environment, the economy and society.

The regression model for housing attributes and green certification on house price developed in this study could offer valuable benefits to support and advance Malaysia in realising its medium and long-term goals for green technology.

Islamic home financing products with Murabaha contracts are widely favored among the Muslim community in Indonesia, given that the country has a population of over 230 million Muslims. To facilitate the development of products and enhance public interest, it is important for Islamic banking institutions to comprehend the elements that may impact the intents of Muslim communities in Indonesia when selecting Islamic home financing products with Murabaha contracts. The purpose of this study is to ascertain the many aspects that may have an impact on the decision-making process of Muslim communities in Indonesia when selecting Islamic home financing product that use Murabaha contracts.

The partial least square-structural equation modeling data processing techniques will be used to process and evaluate these components. The data used in this study was acquired by administering questionnaires to a sample of 298 Muslim communities, which were randomly selected from a pool of 301 possible customers of Islamic house finance in Indonesia.

The results of this research show that attitude, subjective norms and perceived behavioral control have positive influence on intention to choose an Islamic home financing scheme with Murabaha agreement, while price fairness of Islamic home financing and Islamic altruism have direct and indirect influence on intention to choose Islamic home financing.

Analyzing factors that affect intention to choose Islamic home financing product under Murabaha contract is essential. Future study is required to analyze other Islamic home financing products, such as istisna, ijarah muntahia bi tamlik and diminishing musharakah. This study only serves as a foundation for further investigations into conventional approaches to home financing in emerging nations. The areas can be expanded to be implemented in other countries.

It is anticipated that Islamic banks have the capacity to cultivate a favorable and constructive perception, hence fostering a positive disposition among the Muslim populace in Indonesia. Furthermore, it is essential for Islamic banks to guarantee that all stakeholders within the sharia-compliant institution, particularly the frontline staff, have enough expertise and understanding of the intricacies of Islamic home financing products including Murabaha contracts, which are intended for prospective customers. In the foreseeable future, it is anticipated that the Muslim population in Indonesia would exhibit a greater intention toward the use of Islamic home financing solutions that use Murabaha contracts, facilitated by the establishment of a conducive environment.

This research integrates the impacts of pricing fairness and Islamic charity as a modified model, alongside the theory of planned behavior model, to examine the influence of these factors on individuals’ intentions to use Islamic home financing in Indonesia.

The Saudi 2030 Housing Program Vision aims to increase the population of Riyadh City, the capital of the Kingdom of Saudi Arabia, to between 15 and 20 million people. This paper aims to predict the demand for residential units in Riyadh City by 2030 in line with this vision.

This paper adopts a statistical modeling approach to estimate the residential demands for Riyadh City. Several population growth models, including the nonlinear quadratic polynomial spline regression model, the sigmoidal logistic power model and the exponential model, are tested and applied to Riyadh to estimate the expected population in 2030. The growth model closest to the Kingdom’s goal of reaching between 15 and 20 million people in 2030 is selected, and the paper predicts the required number of residential units for the population obtained from the selected model. Desktop database research is conducted to obtain the data required for the modeling and analytical stage.

The exponential model predicts a population of 16,476,470 in Riyadh City by 2030, and as a result, 2,636,235 household units are needed. This number of housing units required in Riyadh City exceeds the available residential units by almost 1,370,000, representing 108% of the available residential units in Riyadh in 2020.

This study provides valuable insights into the demand for residential units in Riyadh City by 2030 in line with the Saudi 2030 Housing Program Vision, filling the gap in prior research. The findings suggest that significant efforts are required to meet the housing demand in Riyadh City by 2030, and policymakers and stakeholders need to take appropriate measures to address this issue.

The purpose of this study is to understand the investment decision-making of real estate investors in housing, highlighting the interplay between rational and irrational factors. In this study, investment satisfaction was a mediator, while reinvestment intention was the dependent variable.

A quantitative, cross-sectional and descriptive research design was used, gathering data from a sample of 550 residential real estate investors using a multi-stage stratified sampling technique. The partial least squares structural equation modelling disjoint two-stage approach was used for data analysis. This methodological approach allowed for an in-depth examination of the relationship between rational factors such as location, profitability, financial viability, environmental considerations and legal aspects alongside irrational factors including various biases like overconfidence, availability, anchoring, representative and information cascade.

This study strongly supports the adaptive market hypothesis, showing that residential real estate investor behaviour is dynamic, combining rational and irrational elements influenced by evolutionary psychology. This challenges traditional views of investment decision-making. It also establishes that behavioural biases, key to adapting to market changes, are crucial in shaping residential property market efficiency. Essentially, the study uncovers an evolving real estate investment landscape driven by evolutionary behavioural patterns.

This research redefines rationality in behavioural finance by illustrating psychological biases as adaptive tools within the residential property market, urging a holistic integration of these insights into real estate investment theories.

The study reshapes property valuation models by blending economic and psychological perspectives, enhancing investor understanding and market efficiency. These interdisciplinary insights offer a blueprint for improved regulatory policies, investor education and targeted real estate marketing, fundamentally transforming the sector’s dynamics.

Unlike previous studies, the research uniquely integrates human cognitive behaviour theories from psychology and business studies, specifically in the context of residential property investment. This interdisciplinary approach offers a more nuanced understanding of investor behaviour.

Land value in urban areas in India is influenced by regulations, bylaws and the amenities associated with them. Planning interventions play a significant role in enhancing the quality of the neighbourhood, thereby resulting in a change in its value. Land is a distinct commodity due to its fixed location, and planning interventions are also specific to certain locations. Consequently, the factors influencing land value will vary across different areas. While recent literature has explored some determinants of land value individually, conducting a comprehensive study specific to each location would be more beneficial for making informed policy decisions. Therefore, this article aims to examine and identify the critical factors that impact the value of residential land in the National Capital Territory of Delhi, India.

The study employed a combination of semi-structured and structured interview methods to construct a Relative Importance Index (RII) and ascertain the critical determinants affecting residential land value. A sample of 36 experts, comprising property valuers, urban planners and real estate professionals operating within the National Capital Territory of Delhi, India, were selected using snowball sampling techniques. Subsequently, rank correlation and ANOVA methods were employed to evaluate the obtained results.

Location and stage of urban development are the most critical determinants in determining residential land values in the National Capital Territory of Delhi, India. The study identifies a total of 13 critical determinants.

A scenario planning approach can be developed to achieve an equitable distribution of values and land use entropy. A land value assessment model can also be developed to assist professional valuers.

There has been a lack of emphasis on assessing the impact of planning interventions and territorial regulation on land values in the context of Delhi. This study will contribute to policy decision-making by developing a rank list of planning-based determinants of land value.

Housing interventions carried out in accordance with current regulations in the Old City of Aleppo, both before and after the Syrian war, are minor in comparison to those carried out without a license and illegally. This suggests current policies are inadequate and needs upgrading.

This article critically reviews current Syrian policies and their implementation on residential heritage in the Old City of Aleppo with the aim to identify gaps and propose directions for modifications. Next to a review of the text of official policies and implementation documents, the archive of the Directorate of the Old City has been consulted and license applications, presented in the period 2018–2022, have been examined. Moreover, interviews with decision-makers from academics and practice were conducted.

Major limitations of these policies and relative application procedures have been identified: these involve: legal/administrative, economic and social aspects.

The specific needs have been highlighted and some proposals for improvement made.