Recruitment Challenges in the Indian Real Estate Sector: An Exploratory Study of Human Resource Practices at Real estate company

Recruitment remains one of the most critical yet challenging functions of human resource management, particularly in sectors experiencing rapid growth and intense competition for talent, such as real estate and construction. This study examines the recruitment challenges faced by human resource (HR) professionals at one of the real estate companies, a leading real estate and construction organisation in India, with a focus on sourcing difficulties, candidate–employer expectation gaps, and the role of technology in the hiring process. A descriptive, cross-sectional research design was employed, and primary data were collected through a structured questionnaire administered to 36 HR professionals including recruiters, executives, associates, talent acquisition specialists, and managers selected through purposive (judgment) sampling. Responses were analysed using descriptive statistics (frequencies and percentages). The findings indicate that talent shortage and intense market competition are the most significant barriers to effective hiring, while internal coordination between HR and hiring managers, interview scheduling, and candidates' understanding of job roles are comparatively well managed. Recruitment technology, including applicant tracking systems and video-based assessments, is perceived positively, with a large majority of respondents reporting that it improves screening efficiency and reduces bias in shortlisting. Salary mismatches, extended candidate notice periods, and occasional skill gaps were identified as moderate but manageable challenges. The study contributes to the limited empirical literature on recruitment practices within the Indian real estate sector and offers practical recommendations for strengthening sourcing strategies, internal coordination, and technology adoption. As an exploratory, single-organisation study with a modest, non-probability sample, the findings should be interpreted as indicative rather than statistically generalisable, and directions for future research using larger, multi-organisational, and probability-based samples are proposed.