A hybrid neural network for predicting Days on Market a measure of liquidity in real estate industry

Kamara, Amadu Fullah; Chen, Enhong; Liu, Qi; Pan, Ziyu

doi:10.1016/j.knosys.2020.106417

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…In addition, SVM needs to find suitable kernel functions, such as linear, polynomial, sigmoid, and radial basis functions. In the SVR application, Kamara et al [39] used deep learning models, including SVR, to solve the forecast of the Land 2022, 11, 1138 6 of 17 number of days on sale, and their results provide sellers the ability to adjust their prices in response to expected sales.…”

Section: Support Vector Regression (Svr)mentioning

confidence: 99%

An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices

Cheng

Tsai

2022

Land

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Accurately forecasting housing prices will enable investors to attain profits, and it can provide information to stakeholders that housing prices in the community are falling, stabilizing, or rising. Previous studies on housing price forecasting mostly used hedonic pricing and weighted regression methods, which led to the lack of consideration of the nonlinear relationship model and its explanatory power. Furthermore, the attribute data of housing price forecasts are a heterogeneous study, and they are difficult to forecast accurately. Therefore, this study proposes an intelligent homogeneous model based on an enhanced weighted kernel self-organizing map (EW-KSOM) for forecasting house prices; that is, this study proposes an EW-KSOM algorithm to cluster the collected data and then applies random forest, extra tree, multilayer perception, and support vector regression to forecast the house prices of full, district, and apartment complex data. In the experimental comparison, we compare the performance of the proposed enhanced weighted kernel self-organizing map with the listing clustering methods. The results show that the best forecast algorithm is the combined EW-KSOM and random forest under the root mean square error and root-relative square error, and the proposed method can effectively improve the forecast capability of housing prices and understand the influencing factors of housing prices in full and important districts. Furthermore, we obtain that the top five key factors influencing house prices are transferred land area, house age, building transfer total area, population percentage, and the total number of floors. Lastly, the research results can provide references for investors and related organizations.

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Section: Support Vector Regression (Svr)mentioning

confidence: 99%

An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices

Cheng

Tsai

2022

Land

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“…Based on the DEA-Malmquist method, Chen et al predicted corporate assets by analyzing the inventory manifestation of the Chinese real estate industry from 2005-2015, concluding that there may be zombie enterprises and the risk of future unemployment [6]. Kamara et al proposed a new hybrid neural network model with CNN attention (CNNA) and bidirectional LSTM (BLSTM)-based modules to extract features to tackle the Day-of-Market (DOM) prediction problem [7]. According to the estimated distribution of the characteristics, confidence intervals for the four properties in the dataset were derived from percentile Bootstrap confidence intervals (CI) or percentile bias-corrected accelerations' (BCA) Bootstrap CI.…”

Section: Introductionmentioning

confidence: 99%

Construction and Simulation of the Market Risk Early-Warning Model Based on Deep Learning Methods

Lei

2022

Scientific Programming

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To address the problem of low efficiency of existing forecasting models for market risk warning, a market risk early-warning model based on improved LSTM is suggested utilizing the whale optimization algorithm (WOA) to optimize the number of hidden layer neurons and time step parameters of long short-term memory. The proposed market risk early-warning model is validated by using 40 real estate companies as the research subjects and 20 relevant variables such as gross operating income, net profit asset growth rate, and total asset growth rate as indicators. The results demonstrate that the proposed model’s prediction accuracy for market risk is greater than 96% and that when compared to the standard CNN and LSTM models, the suggested model’s prediction accuracy for corporate finance from 2012 to 2019 is increased by 14% and 12%, respectively, and the prediction accuracy for corporate finance in 2020 is improved by 22% and 7%, respectively, which has certain practical application value and superiority.

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“…The results encourage to incorporate more advanced prediction algorithms and methods into the data set. Another research went further and combined percentile and LDA to predict Days on Market, which is a measure of liquidity in the real estate industry [19].…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model

Siahaan¹,

Darnoto²

2022

Preprint

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Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.

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A hybrid neural network for predicting Days on Market a measure of liquidity in real estate industry

Cited by 11 publications

References 27 publications

An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices

An Intelligent Homogeneous Model Based on an Enhanced Weighted Kernel Self-Organizing Map for Forecasting House Prices

Construction and Simulation of the Market Risk Early-Warning Model Based on Deep Learning Methods

A Novel Framework to Detect Irrelevant Software Requirements Based on MultiPhiLDA as the Topic Model

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