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The purpose of the study is to examine the value-based performance of firms in construction sector in India using Tobin's Q and Market Capitalization (MCAP) and then determine their significant financial drivers.

The study is based on data from 87 firms engaged in infrastructure, real estate, industrial construction and allied areas in India over a study period of 10 years. Three distinct forms of panel regression models have been developed using Tobin's Q and MCAP as dependent variables. The models developed are using Baltagi's (1981) Error Component 2SLS, Varadharajan-Krishnakumar's (1987) Generalized 2SLS and Arellano – Bower/Blundell – Bond's (1991) dynamic panel.

The study found that MCAP is a better suited value-based performance measure for construction sector firms in India. The study further reports that the age of the firm, profit after tax, investment in research and development, dividends, leverage and net fixed asset are significant positive drivers, whereas cash flow is a significant negative driver.

The study is limited to a geographic location; therefore, the findings of this study cannot be generalized.

As MCAP is a better suited value-based performance measure of a firm in the construction sector, managers should focus on improving profitability, higher research and development activities, higher dividends and higher expenditures on net fixed assets for improvement.

This is an original attempt to examine the value-based performance of firms in the construction sector in India using Tobin's Q and MCAP.

The previous researchers have identified human capital, relational capital and structural capital as knowledge assets in knowledge-driven organizations. The current study is an attempt to identify and validate the knowledge assets in construction projects. The study also aims to understand the interrelation of these knowledge assets and their impact on project performance through the development of a conceptual model.

The study is divided into three phases. In phase I, the constructs of “knowledge assets” and “project performance” in construction projects are identified using the exploratory factor analysis. In phase II, these constructs are validated using confirmatory factor analysis. Two separate surveys are conducted for phase I and phase II, respectively. In phase III, the authors develop two conceptual models based on the literature review and two construction project cases in India. The models examine the inter-relationship of knowledge assets and measures their impact on project performance. The models are empirically tested using the responses of the second survey through a structural equation model.

The study extracts four knowledge asset constructs and one performance construct which are named human capital, structural capital, relational capital, human capital capacity building process and project performance, respectively. The study finds that both the conceptual models are statistically excellent fit. The results of the models suggest that relational capital and structural capital have a direct positive impact on project performance, whereas human capital has an indirect effect on project performance mediated through relational capital, structural capital and human capital capacity building process.

The items for knowledge asset constructs and measurement of project performance are moderated by experts, working in construction projects in India, hence the process may contain subjective bias. Further, two construction project cases were selected by authors in the study that originate from India.

The study has implications for the project executors (contractors) as well as for project owners. The contractors must maintain healthy relations with all the stakeholders in a project like a client, suppliers, architects, etc. They must develop systems that are people-friendly to avoid the problems of time and cost overruns in projects. The owners must also maintain healthy relations. This can result in a win-win situation for both parties and can lead to superior project performance.

The study develops and empirically tests two conceptual models that explain the interrelations of knowledge assets and how it benefits the construction project performance in India. Therefore, the generalization of the results is difficult; however, the results can be replicated in projects with similar settings.

This study aims to investigate whether intellectual capital (IC) and its subcomponents enhance value and improve the profitability of real estate (RE) and infrastructure (INF) firms in India. In this study, IC is measured through the value-added intellectual coefficient (VAIC) model. The study further extends the VAIC model by incorporating an additional component of social welfare efficiency (SWE).

The study uses the panel data investigation based on the data of 63 firms (22 RE and 41 INF firms), for a period of 10 years (2008–2017). The dependent variables in the study are return on assets (ROA) and market price to book value ratio (PB), whereas the independent variables are VAIC and its components. The panel is tested for stationarity, heteroscedasticity and multicollinearity problems. Finally, to account for heteroscedasticity and endogeneity, Arellano and Bond's (1991) panel regression estimator with robust estimates are used.

The findings of the study suggest that IC has a significant influence on the profitability and value of infra firms, whereas capital-employed efficiency (CEE) positively affects the profitability of both RE and INF firms.

The study is an attempt to find the effect of IC and its components on profitability and value of RE and INF firms in India. The author has also extended the VAIC model, which was introduced by Pulic (2000), by adding an additional IC component, i.e. SWE. The study uses Arellano and Bond's (1991) panel regression estimator with robust estimates, which helps produce robust results.

This study aims to examine whether or not residential properties closer to landfill sites have lower offer values by the developers. That is, by analyzing real estate data and landfill site locations, the study seeks to provide insights into whether properties situated closer to landfill sites tend to have a lower offer values than those located farther away.

The study is exploratory in nature, and a case study approach is applied. A landfill site named “Uruli Devachi” is selected in the region of Pune district, and data is collected from 102 developers selling residential projects within a radius of 15 km (about 9.32 mi). The gathered data is analyzed by using basic descriptive statistics, one-way ANOVA and ordinary least squares (OLS) regression. The OLS regression helps to determine whether there is a relationship between the distance of a residential property from a landfill site and its offer value.

The findings suggest that landfill sites have a detrimental impact on residential property offer values, with the negative impact increasing with proximity to a landfill site. The negative effect seems to vanish after over 10 km (about 6.21 mi). The developers provide extra facilities including a clubhouse, a children’s play area, a gym and a swimming pool in an effort to mitigate the negative effects of the landfill site on residential properties.

The findings of this study could have implications for property developers, real estate professionals and policymakers in understanding how landfill proximity might impact property offer values.

This study presents many novelties for the Indian housing market: the landfill sites do have a negative effect on the offer value of residential property; the closer the residential property to a landfill site, the higher the negative effect. Further, the developers try and mitigate the negative effect of landfill sites on residential properties by providing additional amenities such as a clubhouse, children’s play park, gym and swimming pool.

The purpose of this study is to compare the predictive performance of the hedonic multivariate regression model with the probabilistic neural network (PNN)-based general regression neural network (GRNN) model of housing prices in “Pune-India.”

Data on 211 properties across “Pune city-India” is collected. The price per square feet is considered as a dependent variable whereas distances from important landmarks such as railway station, fort, university, airport, hospital, temple, parks, solid waste site and stadium are considered as independent variables along with a dummy for amenities. The data is analyzed using a hedonic type multivariate regression model and GRNN. The GRNN divides the entire data set into two sets, namely, training set and testing set and establishes a functional relationship between the dependent and target variables based on the probability density function of the training data (Alomair and Garrouch, 2016).

While comparing the performance of the hedonic multivariate regression model and PNN-based GRNN, the study finds that the output variable (i.e. price) has been accurately predicted by the GRNN model. All the 42 observations of the testing set are correctly classified giving an accuracy rate of 100%. According to Cortez (2015), a value close to 100% indicates that the model can correctly classify the test data set. Further, the root mean square error (RMSE) value for the final testing for the GRNN model is 0.089 compared to 0.146 for the hedonic multivariate regression model. A lesser value of RMSE indicates that the model contains smaller errors and is a better fit. Therefore, it is concluded that GRNN is a better model to predict the housing price functions. The distance from the solid waste site has the highest degree of variable senstivity impact on the housing prices (22.59%) followed by distance from university (17.78%) and fort (17.73%).

The study being a “case” is restricted to a particular geographic location hence, the findings of the study cannot be generalized. Further, as the objective of the study is restricted to just to compare the predictive performance of two models, it is felt appropriate to restrict the scope of work by focusing only on “location specific hedonic factors,” as determinants of housing prices.

The study opens up a new dimension for scholars working in the field of housing prices/valuation. Authors do not rule out the use of traditional statistical techniques such as ordinary least square regression but strongly recommend that it is high time scholars use advanced statistical methods to develop the domain. The application of GRNN, artificial intelligence or other techniques such as auto regressive integrated moving average and vector auto regression modeling helps analyze the data in a much more sophisticated manner and help come up with more robust and conclusive evidence.

To the best of the author’s knowledge, it is the first case study that compares the predictive performance of the hedonic multivariate regression model with the PNN-based GRNN model for housing prices in India.

The paper aims to identify the critical success factors (CSFs) at an individual level for real estate developers (REDs) in India.

Fifteen individual-level CSFs are identified from literature review. These CSFs are moderated through expert opinion, and they are customized for the real-estate sector. Five-point scale questionnaire is developed and furnished to REDs to understand the importance of these 15 CSFs. Fifty-six REDs responded to the survey. Using the responses from the survey, relative importance index is created for all 15 factors. These factors are also grouped in broad categories using exploratory factor analysis and the groups are further validated through confirmatory factor analysis.

The study finds that leadership quality, man-management skill, disputes resolution skill, ability to take risk and knowledge about construction and finance are the top five CSFs for REDs in India. The exploratory factor analysis resulted in five groups and they are named as “liaising with government,” “relationship management,” “knowledge management,” “skill management” and “ability.” The groups exhibit reasonable reliability and validity.

Despite useful results, study being exploratory in nature is limited because of a small sample size. Despite best efforts, authors find reluctance from REDs to respond to the survey.

The findings of the study are important for REDs and success of their business. The business of REDs can improve if they exhibit leadership quality, man-management skill and disputes resolution skill. The ability of the developers to take risk and their knowledge about construction and finance can also be vital for the success of their business.

To the best of authors’ knowledge, this is the first attempt to identify CSFs for REDs in India.

This paper aims to examine the determinants of the dividend policy of the construction companies in India.

Data from 2011 to 2016 (six years) of 45 listed construction companies in India are collected, and a strong balanced panel is created. Dividend per share is dependent variable, and profitability, unstable earnings, institutional holding, cash flow, tangibility, liquidity, growth opportunities, age of the firm, life cycle, leverage, size of firm and taxation are explanatory variables. The panel is tested for stationarity and finally fixed and random-effect panel regression model with robust estimation option is performed.

The random effect model is found fit with an R² of 62 per cent, and profitability, life cycle and size of the firm show a significant positive effect on dividend payment. Cash flow shows a negative significant relationship, indicating the presence of agency problem. Rest of the variables indicated an insignificant relationship.

The study is carried out on a small sample of 45 companies with data of only six years. Further, there may be behavioral and psychological factors that drive the decision to declare dividend. Those factors have not been considered in present study. Despite considerable efforts, the author could not find more studies specific to the construction sector. Hence, the variables identified in the present study are more generic, even though a few sector-specific studies have been included.

The dividend policy determinants for the construction sector in India are investigated, and a comprehensive model based on 12 explanatory variables is tested to find the drivers of dividend payout in Indian construction companies. From the investor’s point of view, the sector has immense potential in terms of dividend as well as capital appreciation. Therefore, the study can be useful to the investors to understand the drivers of dividend payout in the construction sector. It can also be crucial for companies to create an appropriate dividend policy so as to attract and retain investors. The study contributes significantly to the existing body of knowledge by recommending the salient drivers of dividend payout in the construction sector based on a comprehensive dataset and using robust methodology.

This study aims to compare the short-run performance of construction and non-construction initial public offerings (IPOs) that are offered in India during 2006–2015. The study also attempts to investigate the impact of ownership structure (i.e. concentrated ownership in the hand of promoters and institutional ownership) and market sentiment on the performance of construction sector IPOs in short run.

A total of 281 IPOs were listed at National Stock Exchange, India, during the study period, and 44 of those were from construction sector. The short-run performance of these construction and non-construction IPOs was compared using two indicators, i.e. monthly stock return (SR) and excess return over market benchmark (MAR). To examine the effect of concentrated ownership in the hand of promoters, institutional ownership and market sentiment on IPO performance, systematic dynamic panel regression model was developed.

The IPOs of construction firms perform significantly better than the non-construction firms. The performance of construction IPOs is significantly driven by the lag effect. This suggests a significant informational inefficiency, which results in a delayed reaction by investors. The market sentiment has a positive influence on the performance of construction sector IPOs, whereas the institutional holding has a negative influence.

To the best of the author’s knowledge, this study is the first attempt to examine the performance of construction sector IPOs in short run. The study uses systematic dynamic panel data regression, which provides better and reliable estimates.

The study aims to find if family-owned construction and real estate firms in India are more profitable compared to non-family-owned construction and real estate firms. The study also examines if family ownership and institutional ownership are drivers of the firm profitability.

The study uses data of 199 construction and real estate firms listed on the National Stock Exchange (NSE), India. The data pertains to a period of 13 years (2006-2018). The family firm is defined on the basis on ownership criteria, and the sample is divided into two groups, namely, family firms and non-family firms. The data is analyzed using a two-sample t-test assuming unequal variance and Prais–Winsten panel regression using correlated panels with corrected standard errors (PCSEs) procedure.

The findings suggest that family-owned construction and real estate firms are slightly more profitable compared to non-family-owned construction and real estate firms; however, family firms command lesser valuation in the market. The reason for this lower valuation is the mismatch between family holding and institutional holding. A family firm’s profitability is primarily driven by institutional holding that acts as mitigation against the agency conflict.

The paper is the first attempt to analyze the profitability of construction and real estate family firms, and compare it with non-family-owned construction and real estate firms.

This study aims to identify the most profitable segment of construction firms amongst real estate, industrial construction and infrastructure. This paper also examines the determinants of profitability of real estate, industrial construction and infrastructure firms.

The data of 67 firms (20 real estate, 21 industrial construction and 26 infrastructure) is collected for a 15-year period (2003–2017). Two models are created using total return on assets (ROA) and return on invested capital (ROIC) as dependent variables.. Leverage, liquidity, age, growth, size and efficiency of the firm are identified as firm-specific independent variables. Two economic variables, i.e. growth in GDP and inflation, are also used as independent variables. Initially, the models are tested for stationarity, multicollinearity and heteroscedasticity, and finally, the coefficients are estimated using Arellano–Bond dynamic panel data estimation to account for heteroscedasticity and endogeneity.

The results suggest that industrial construction is the most profitable segment of construction, followed by real estate and infrastructure. Their profitability is positively driven by liquidity, efficiency and leverage. The real estate firms are somewhat less profitable compared to industrial construction firms, and their profitability is positively driven by liquidity. The infrastructure firms have low ROA and ROIC.

The real estate, infrastructure and industrial construction drastically differ from each other. The challenges involved in real estate, infrastructure and industrial construction are altogether different. Therefore, authors present a comparative analysis of the profitability of real estate, infrastructure and industrial construction segments of the construction and compare their determinants of profitability. The results provided in the study are robust and reliable because of the use of a superior econometric model, i.e. Arellano–Bond dynamic panel data estimation with robust estimates, which accounts for heteroscedasticity and endogeneity in the model.