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Recommender Systems (RSs) have become an essential part of most e-commerce sites nowadays. Though there are several studies conducted on RSs, a hybrid recommender system for the real state search engine to find appropriate rental apartment taking users preferences into account is still due. To address this problem, a hybrid recommender system is proposed in this paper constructed by two of the most popular recommendation approaches — Collaborative Filtering (CF), Content-Based Recommender (CBR). CF-based methods use the ratings given to items by users as the sole source of information for learning to make a recommendation. However, these ratings are often very sparse in applications like a search engine, causing CF-based methods to degrade accuracy and performance. To reduce this sparsity problem in the CF method, the Cosine Similarity Score (CSS) between the user and predicted apartment, based on their Feature Vectors (FV) from the CBR module is utilized. Improved and optimized Singular Value Decomposition (SVD) with Bias-Matrix Factorization (MF) of the CF model and CSS with FV of CBR constructs this hybrid recommender. The proposed recommender was evaluated using the Statistical Cross-Validation consisting of Leave-One-Out Validation (LOOCV). Experimental results show that it significantly outperformed a benchmark random recommender in terms of precision and recall. In addition, a graphical analysis of the relationships between the accuracy and error minimization is presented to provide further evidence for the potentiality of this hybrid recommender system in this area.