A Novel Prediction Model for the Sales Cycle of Second-Hand Houses Based on the Hybrid Kernel Extreme Learning Machine Optimized Using the Improved Crested Porcupine Optimizer

Second-hand housing transactions are an important part of the housing market. Due to the dual influence of location and price, the sales cycle of second-hand housing has shown significant diversity. As a result, when residents sell or buy second-hand houses, they often cannot accurately and quickly evaluate the cycle of the second-hand house; thus, the transaction fails. For this reason, this paper develops a prediction model of the second-hand housing sales cycle based on the hybrid kernel extreme learning machine (HKELM) optimized using the Improved Crested Porcupine Optimizer (CPO), which has achieved rapid and accurate prediction. Firstly, this paper uses a Stimulus–Organism–Response model to identify 33 factors that affect the second-hand housing sales cycle from three aspects: policy factors, economic factors, and market supply and demand. Then, in order to solve the problems of slow convergence, easy-to-fall-into local optimum, and insufficient optimization performance of the traditional CPO, this paper proposes an improved optimization algorithm for crowned porcupines (Cubic Chaos Mapping Crested Porcupine Optimizer, CMTCPO). Subsequently, this paper puts forward a prediction model of the second-hand housing sales cycle based on an improved CPO-HKELM. The model has the advantages of a simple structure, easy implementation, and fast calculation speed. Finally, this paper selects 400 second-hand houses in eight cities in China as case studies. The case study shows that the maximum relative error based on the model proposed in this paper is only 0.0001784. A ten-fold cross-test proves that the model does not have an over-fitting phenomenon and has high reliability. In addition, this paper discusses the performances of different chaotic maps to improve the CPO and proves that the algorithm including chaotic maps, mixed mutation, and tangent flight has the best performance. Compared with the classical meta-heuristic optimization algorithm, the improved CPO proposed in this paper has the smallest calculation error and the fastest convergence speed. Compared with a BPNN, LSSVM, RF, XGBoost, and LightGBM, the HKELM has advantages in prediction performance, being able to handle high-dimensional complex data sets more effectively and significantly reduce the consumption of computing resources. The relevant research results of this paper are helpful to predict the second-hand housing sales cycle more quickly and accurately.