Enhancing Rental Search Experience in Nigeria: Applying Deep Content-based Filtering for Personalised House Recommendations
Chapter One
OBJECTIVE OF THE STUDY
The objective of this study is to examine Enhancing Rental Search Experience in Nigeria: Applying Deep Content-Based Filtering for Personalised House Recommendations.
The specific objectives are to:
- Identify the challenges faced by renters in using existing digital rental search platforms in Nigeria.
- Examine the limitations of current search and filtering mechanisms in Nigerian real estate platforms.
- Develop a deep content-based filtering framework for personalised house recommendations using structured and unstructured rental property data.
- Evaluate the effectiveness of the proposed deep content-based filtering model in improving search accuracy, relevance, and user satisfaction compared to traditional methods.
CHAPTER TWO
REVIEW OF RELATED LITERATURE
Rental Search Experience
The rental search experience refers to the overall process, perceptions, and outcomes encountered by individuals when seeking rental housing. It encompasses the ease of access to information, efficiency of search tools, accuracy of property listings, transparency of transactions, and the level of satisfaction derived from the housing search journey (Sirmans et al., 2019). In essence, it is not only about finding a house but also about how convenient, reliable, and personalized the process is for the renter.
In many housing markets, the rental search experience is shaped by both supply-side factors (availability, affordability, and quality of properties) and demand-side factors (tenant preferences, financial capacity, and location priorities) (Teye & Alabi, 2018). When information about available properties is incomplete, outdated, or misleading, tenants face information asymmetry, which increases the cost, stress, and risks associated with renting (Akinyemi, 2021). For example, in Nigeria, challenges such as fraudulent listings, lack of standardized property data, and dependence on middlemen often worsen the experience of house-hunting (Oshodi & Ojo, 2022).
The emergence of digital rental platforms and property technology (proptech) has transformed the rental search process by providing online access to listings, photos, and filtering tools. However, most platforms in developing countries like Nigeria still rely on basic search mechanisms such as location, price, and number of rooms, which often fail to capture deeper tenant preferences such as proximity to social amenities, lifestyle compatibility, or aesthetic design (Bello & Usman, 2023). This often results in information overload, where renters are forced to sift through numerous irrelevant listings before finding a suitable property (Nguyen & Ricci, 2018).
Globally, research in recommender systems and housing technologies emphasizes the importance of personalization in enhancing the rental search experience. Personalized search tools that integrate machine learning and artificial intelligence can improve user satisfaction by providing tailored property suggestions that align with both explicit preferences (budget, location) and implicit preferences (design style, neighborhood quality) (Zhang et al., 2019). Deep content-based filtering, in particular, enables rental platforms to analyze structured data (e.g., price, size, location) alongside unstructured data (e.g., images, descriptions), thereby improving the relevance of search results (Sun et al., 2020).
CHAPTER THREE
RESEARCH METHODOLOGY
Advertisements
INTRODUCTION
In this chapter, we described the research procedure for this study. A research methodology is a research process adopted or employed to systematically and scientifically present the results of a study to the research audience viz. a vis, the study beneficiaries.
RESEARCH DESIGN
Research designs are perceived to be an overall strategy adopted by the researcher whereby different components of the study are integrated in a logical manner to effectively address a research problem. In this study, the researcher employed the survey research design. This is due to the nature of the study whereby the opinion and views of people are sampled. According to Singleton & Straits, (2009), Survey research can use quantitative research strategies (e.g., using questionnaires with numerically rated items), qualitative research strategies (e.g., using open-ended questions), or both strategies (i.e., mixed methods). As it is often used to describe and explore human behaviour, surveys are therefore frequently used in social and psychological research.
POPULATION OF THE STUDY
According to Udoyen (2019), a study population is a group of elements or individuals as the case may be, who share similar characteristics. These similar features can include location, gender, age, sex or specific interest. The emphasis on study population is that it constitutes of individuals or elements that are homogeneous in description.
This study was carried to examine Enhancing Rental Search Experience in Nigeria: Applying Deep Content-Based Filtering for Personalized House Recommendations. Selected renters in Abuja capital city form the population of the study.
CHAPTER FOUR
DATA PRESENTATION AND ANALYSIS
INTRODUCTION
This chapter presents the analysis of data derived through the questionnaire and key informant interview administered on the respondents in the study area. The analysis and interpretation were derived from the findings of the study. The data analysis depicts the simple frequency and percentage of the respondents as well as interpretation of the information gathered. A total of eighty (80) questionnaires were administered to respondents of which only seventy-seven (77) were returned and validated. This was due to irregular, incomplete and inappropriate responses to some questionnaire. For this study a total of 77 was validated for the analysis.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
Introduction
It is important to ascertain that the objective of this study was to Enhancing Rental Search Experience in Nigeria: Applying Deep Content-Based Filtering for Personalized House Recommendations. In the preceding chapter, the relevant data collected for this study were presented, critically analyzed and appropriate interpretation given. In this chapter, certain recommendations made which in the opinion of the researcher will be of benefits in addressing Enhancing Rental Search Experience in Nigeria: Applying Deep Content-Based Filtering for Personalized House Recommendations.
Summary
This study was on Enhancing Rental Search Experience in Nigeria: Applying Deep Content-Based Filtering for Personalized House Recommendations. Four objectives were raised which included: Identify the challenges faced by renters in using existing digital rental search platforms in Nigeria, Examine the limitations of current search and filtering mechanisms in Nigerian real estate platforms, Develop a deep content-based filtering framework for personalized house recommendations using structured and unstructured rental property data and Evaluate the effectiveness of the proposed deep content-based filtering model in improving search accuracy, relevance, and user satisfaction compared to traditional methods. A total of 77 responses were received and validated from the enrolled participants where all respondents were drawn from selected renters in Abuja. Hypothesis was tested using Chi-Square statistical tool (SPSS).
Conclusion
This study examined the challenges faced by renters in using existing digital rental search platforms in Nigeria and identified the limitations of current search and filtering mechanisms. The findings revealed that while digital platforms such as PropertyPro, PrivateProperty, and Jiji have improved access to property listings, renters continue to encounter issues such as incomplete or outdated information, lack of personalized recommendations, high search costs, fraudulent listings, and limited transparency. Furthermore, the filtering mechanisms currently deployed on most Nigerian real estate platforms are rudimentary, relying mainly on basic parameters such as price, location, and property type, which fail to capture deeper user preferences and contextual needs. The study also highlighted that the absence of advanced recommendation systems has reduced search efficiency, resulting in renter frustration and decreased trust in digital rental platforms. Against this backdrop, the adoption of deep content-based filtering models that integrate both structured and unstructured rental property data offers a promising solution. Such a system has the potential to enhance search accuracy, improve recommendation relevance, and boost overall user satisfaction by aligning property listings more closely with renters’ needs
Recommendation
Real estate platforms in Nigeria should incorporate deep content-based filtering that combines structured (price, location, property type) and unstructured (descriptions, images, user reviews) data to deliver more personalized and relevant property recommendations
Platforms should introduce stronger verification systems to minimize fraudulent listings and ensure that property information provided is accurate, up-to-date, and trustworthy
Leveraging NLP and deep learning techniques will enable platforms to analyze textual descriptions and property images more effectively, thereby enriching the recommendation process and aligning results with renters’ hidden preferences.
References
- Adomavicius, G., & Tuzhilin, A. (2005). Personalization technologies: A process-oriented perspective. Communications of the ACM, 48(10), 83–90.
- Chi, E. H., Pirolli, P., Chen, K., & Pitkow, J. (2001). Using information scent to model user information needs and actions on the web. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 490–497.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
- Gigerenzer, G., & Selten, R. (2002). Bounded Rationality: The Adaptive Toolbox. MIT Press.
- Pirolli, P., & Card, S. (1999). Information foraging. Psychological Review, 106(4), 643–675.
- Ricci, F., Rokach, L., & Shapira, B. (2015). Recommender Systems Handbook. Springer.
- Simon, H. A. (1979). Rational decision making in business organizations. American Economic Review, 69(4), 493–513.
- Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.
- Adedokun, O., & Akinola, R. (2020). Housing markets and digital transformation in Nigeria: Emerging opportunities and challenges. Journal of Urban Development Studies, 8(2), 55–68.
