Boston Housing Research Articles

Factors affected housing prices: taking Boston as an example

Abstract. Real estate price prediction plays a vital role in urban planning, investment decision-making, and risk management. However, existing prediction models often show problems such as insufficient generalization ability and susceptibility to outliers when faced with complex nonlinear relationships, multidimensional features, and noisy data. Therefore, choosing a model that can accurately capture complex patterns and has strong robustness has become the focus of research. This paper introduces the random forest model and compares it with multivariate linear regression, XGBoost, and support vector machine (SVM). Compared with the traditional regression model, the random forest model combines the flexibility of decision trees and the multi-level feature extraction ability of deep learning, and can better handle the complex nonlinear relationships in the Boston housing price dataset. The experimental results show that the random forest model has achieved excellent performance in all evaluation indicators, and the model accuracy indicators are distributed as MSE=8.2502, RMSE=2.8723, MAE=2.0668, and R^2=0.8875. These results show that the random forest model not only outperforms other models in prediction accuracy but also shows significant advantages in dealing with data complexity and improving generalization ability. Therefore, the random forest model provides an efficient and reliable tool for future real estate price prediction research and applications.

AnFiS-MoH: Systematic exploration of hybrid ANFIS frameworks via metaheuristic optimization hybridization with evolutionary and swarm-based algorithms

The adaptive neuro-fuzzy inference system (ANFIS) has shown promising performance in modeling nonlinear problems, leveraging the strengths of both neural networks and fuzzy inference systems. However, as the problem scale increases, the growing number of tunable parameters in ANFIS can make it challenging to optimize via traditional gradient-based methods alone. This study introduces AnFiS-MoH, a novel framework that synergistically integrates ANFIS with metaheuristic optimization algorithms to address these challenges. By leveraging the global search capabilities of metaheuristics such as ant colony optimization (ACO), particle swarm optimization (PSO), genetic algorithm (GA), and simulated annealing (SA), ANFIS-MOH enhances the parameter tuning process of ANFIS models. We evaluate ANFIS-MOH on benchmark datasets including Boston Housing and Wine Quality, demonstrating significant improvements in prediction accuracy and generalization compared to traditional ANFIS and neural network approaches. The proposed framework achieves up to 20% reduction in Mean Squared Error and 15% increase in R2 scores, particularly excelling in handling high-dimensional, noisy data. This work contributes to the field of hybrid intelligent systems by introducing effective ways to combine the strengths of ANFIS with powerful metaheuristic optimization algorithms. The findings suggest that such hybrid approaches can be effective in tackling challenging nonlinear modeling problems. Our code is available at https://github.com/AmbitYuki/Metaheuristic-Adaptive-ANFIS.

An adaptive trimming approach to Bayesian additive regression trees

A machine learning technique merging Bayesian method called Bayesian Additive Regression Trees (BART) provides a nonparametric Bayesian approach that further needs improved forecasting accuracy in the presence of outliers, especially when dealing with potential nonlinear relationships and complex interactions among the response and explanatory variables, which poses a major challenge in forecasting. This study proposes an adaptive trimmed regression method using BART, dubbed BART(Atr) to improve forecasting accuracy by identifying suspected outliers effectively and removing these outliers in the analysis. Through extensive simulations across various scenarios, the effectiveness of BART(Atr) is evaluated against three alternative methods: default BART, robust linear modeling with Huber’s loss function, and data-driven robust regression with Huber’s loss function. The simulation results consistently show BART(Atr) outperforming the other three methods. To demonstrate its practical application, BART(Atr) is applied to the well-known Boston Housing Price dataset, a standard regression analysis example. Furthermore, random attack templates are introduced on the dataset to assess BART(Atr)’s performance under such conditions.

A Semiparametric Bayesian Approach to Heterogeneous Spatial Autoregressive Models.

Many semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis. Therefore, when considering SSAR models in this paper, it is allowed that the variance parameters of the models can depend on the explanatory variable, and these are called heterogeneous semiparametric spatial autoregressive models. In order to estimate the model parameters, a Bayesian estimation method is proposed for heterogeneous SSAR models based on B-spline approximations of the nonparametric function. Then, we develop an efficient Markov chain Monte Carlo sampling algorithm on the basis of the Gibbs sampler and Metropolis-Hastings algorithm that can be used to generate posterior samples from posterior distributions and perform posterior inference. Finally, some simulation studies and real data analysis of Boston housing data have demonstrated the excellent performance of the proposed Bayesian method.

Parameter Estimation in Spatial Autoregressive Models with Missing Data and Measurement Errors

This study addresses the problem of parameter estimation in spatial autoregressive models with missing data and measurement errors in covariates. Specifically, a corrected likelihood estimation approach is employed to rectify the bias in the log-maximum likelihood function induced by measurement errors. Additionally, a combination of inverse probability weighting (IPW) and mean imputation is utilized to mitigate the bias caused by missing data. Under several mild conditions, it is demonstrated that the proposed estimators are consistent and possess oracle properties. The efficacy of the proposed parameter estimation process is assessed through Monte Carlo simulation studies. Finally, the applicability of the proposed method is further substantiated using the Boston Housing Dataset.

Jackknife empirical likelihood for the correlation coefficient with multiplicative distortion measurement errors

In this paper, we consider the estimation problem of a correlation coefficient between two unobserved variables of interest that are distorted in a multiplicative way by some unobserved confounding variable. We investigate the direct plug-in estimator of the correlation coefficient. We propose using jackknife empirical likelihood (JEL) and its variations to construct confidence intervals for the correlation coefficient based on the estimator. The proposed JEL statistic is shown to be asymptotically a standard chi-squared distribution. We compare our methods to the previous empirical likelihood (EL) techniques of Zhang et al. (2014, ‘A Revisit to Correlation Analysis for Distortion Measurement Error Data’, Journal of Multivariate Analysis, 124, 116–129) and show the JEL possesses better small sample properties. Simulation studies are conducted to examine the performance of the proposed estimator, and we also use our proposed methods to analyse the Boston housing data for illustration.

Analysis of the Influencing Factors and Predictions of Poston house Prices based on a Multiple Linear Regression Model

In this study, the classic Boston house price data set is selected for the analysis of house price correlation. According to the variables in the Boston housing price data set, the linear regression model of Boston housing prices is established by using Python software. The regression equation and regression coefficient were tested for significance, excluding the variable of p >=0.5, multiple linear regression was carried out, and the regression equation with good fitting was obtained. It is found that there are too many variables after multiple linear regression, which is difficult to analyze and predict, so the correlation analysis of variables is carried out. This paper gets the percentage of lower status population, pupil-teacher ratio and average number of rooms per dwelling with medv (The median quoted price for an owner-occupied home, $1,000 per unit) has a significant relationship. Finally, a linear regression equation is established for the independent variable whose correlation coefficient is greater than 0.5, and the housing price is predicted.

Integrating Dropout and Kullback-Leibler Regularization in Bayesian Neural Networks for improved uncertainty estimation in Regression

The objective of the study is to enhance uncertainty prediction in regression problems by introducing a revolutionary Bayesian Neural Network (BNN) model. Experimental results reveal significant improvements in uncertainty prediction and point forecasts with the integrated BNN model compared to the plain BNN. Performance metrics, including mean squared error (MSE), mean absolute error (MAE), and R-squared (R²), demonstrate superior results for the proposed BNN. The experimental results show that for plain BNN, MSE is 87.3, MAE is 6.62 and R2 is −0.0492, whereas for the proposed BNN model MSE was found to be 44.64, MAE is 4.4 and R2 is 0.46. This research brings a fresh approach to Bayesian Neural Networks by incorporating both dropout and KL regularization techniques, resulting in a powerful tool for handling regression tasks with certainty. By combining these techniques, the study enhances model stability, avoids overfitting, and achieves more reliable uncertainty estimation. This study adds to our knowledge of uncertainty-aware machine learning models and offers a valuable solution for accurately assessing uncertainty in various applications.•The innovative BNN model merges the power of Bayesian principles with the effectiveness of dropout and KL regularization.•To test and refine our model, the study utilizes the Boston Housing dataset for both training and evaluation purposes.

Improved Regression Analysis with Ensemble Pipeline Approach for Applications across Multiple Domains

In this research, we introduce two new machine learning regression methods: the Ensemble Average and the Pipelined Model. These methods aim to enhance traditional regression analysis for predictive tasks and have undergone thorough evaluation across three datasets, Kaggle House Price, Boston House Price, and California Housing, using various performance metrics. The results consistently show that our models outperform existing methods in terms of accuracy and reliability across all three datasets. The Pipelined Model, in particular, is notable for its ability to combine predictions from multiple models, leading to higher accuracy and impressive scalability. This scalability allows for their application in diverse fields like technology, finance, and healthcare. Furthermore, these models can be adapted for real-time and streaming data analysis, making them valuable for applications such as fraud detection, stock market prediction, and IoT sensor data analysis. Enhancements to the models also make them suitable for big data applications, ensuring their relevance for large datasets and distributed computing environments. It is important to acknowledge some limitations of our models, including potential data biases, specific assumptions, increased complexity, and challenges related to interpretability when using them in practical scenarios. Nevertheless, these innovations advance predictive modeling, and our comprehensive evaluation underscores their potential to provide increased accuracy and reliability across a wide range of applications. The results indicate that the proposed models outperform existing models in terms of accuracy and robustness for all three datasets. The source code can be found at https://huggingface.co/DebajyotyBanik/Ensemble-Pipelined-Regression/tree/main

Empirical-Likelihood-Based Inference for Partially Linear Models

Partially linear models find extensive application in biometrics, econometrics, social sciences, and various other fields due to their versatility in accommodating both parametric and nonparametric elements. This study aims to establish statistical inference for the parametric component effects within these models, employing a nonparametric empirical likelihood approach. The proposed method involves a projection step to eliminate the nuisance nonparametric component and utilizes an empirical-likelihood-based technique, along with the Bartlett correction, to enhance the coverage probability of the confidence interval for the parameter of interest. This method demonstrates robustness in handling normally and non-normally distributed errors. The proposed empirical likelihood ratio statistic converges to a limiting chi-square distribution under certain regulations. Simulation studies demonstrate that this method provides better inference in terms of coverage probabilities compared to the conventional normal-approximation-based method. The proposed method is illustrated by analyzing the Boston housing data from a real study.

Elastic net-based high dimensional data selection for regression

High-dimensional feature selection is of particular interest to researchers. In some domains, such as microarray data, it is quite common for a group of highly correlated explanatory variables to be of equal importance for inclusion in the predictive model. This paper proposes a new hybrid feature selection approach that integrates feature screening based on Kendall’s tau and Elastic Net regularized regression (K-EN). K-EN as an approach that embeds the Elastic Net, has the advantage of the grouping effect, which automatically includes all the highly correlated variables in the group. The K-EN approach offers insightful solutions to high-dimensional regression problems and improves Elastic Net performance since the screening phase is preceded by a step that further reduces the number of explanatory variables by removing those that disagree with the target based on Kendall’s tau. The use of Kendall’s tau further enhances Elastic Net performance, as it is robust enough to handle heavy-tailed distributions, non-parametric models, outliers, and non-normal data with greater ease. K-EN is therefore a time-saving approach. The proposed algorithm is evaluated on four simulation scenarios and four publicly available datasets, including riboflavin, eyedata, Longley, and Boston Housing, and achieves 0.2528, 0.0098, 0.1007, and 0.4121 respectively as the Mean Squared Error (MSE). K-EN’s MSEs are the best compared to those achieved by the state-of-the-art approaches reviewed in this paper. In addition, K-EN selects up to 100% of relevant features when run on simulated data.

Robust Variable Selection with Exponential Squared Loss for the Spatial Error Model

With the widespread application of spatial data in fields like econometrics and geographic information science, the methods to enhance the robustness of spatial econometric model estimation and variable selection have become a central focus of research. In the context of the spatial error model (SEM), this paper introduces a variable selection method based on exponential square loss and the adaptive lasso penalty. Due to the non-convex and non-differentiable nature of this proposed method, convex programming is not applicable for its solution. We develop a block coordinate descent algorithm, decompose the exponential square component into the difference of two convex functions, and utilize the CCCP algorithm in combination with parabolic interpolation for optimizing problem-solving. Numerical simulations demonstrate that neglecting the spatial effects of error terms can lead to reduced accuracy in selecting zero coefficients in SEM. The proposed method demonstrates robustness even when noise is present in the observed values and when the spatial weights matrix is inaccurate. Finally, we apply the model to the Boston housing dataset.

Research on the Prediction of Boston House Price Based on Linear Regression, Random Rorest, Xgboost and SVM Models

Regression problems, which make up a significant portion of machine learning research, include the Boston housing price forecast problem. Regression problems are a subset of artificial intelligence. Regression problem-solving algorithms that are more frequently used four different machine learning models—the linear regression model, the Random Forest model based on an integrated learning technique, the XGBoost model, and the SVM model—are utilized for training and testing in this study. These models were chosen based on the real-world Boston house price prediction problem. From various angles, the effectiveness of the various algorithmic models on the regression problem of house price prediction was evaluated, and it was determined that among the many algorithmic models. For this problem, the best model is XGBoost Regression and the worst model was the SVM model. The most typical benefit of the XGBoost model is that It can capture complex non-linear relationships between variables.The disadvantage of the SVM model is its computational complexity.

A Novel Deep Kernel Incremental Extreme Learning Machine Based on Artificial Transgender Longicorn Algorithm and Multiple Population Gray Wolf Optimization Methods

Redundant nodes in a kernel incremental extreme learning machine (KI-ELM) increase ineffective iterations and reduce learning efficiency. To address this problem, this study established a novel improved hybrid intelligent deep kernel incremental extreme learning machine (HI-DKIELM), which is based on a hybrid intelligent algorithm and a KI-ELM. First, a hybrid intelligent algorithm was established based on the artificial transgender longicorn algorithm and multiple population gray wolf optimization methods to reduce the parameters of hidden layer neurons and then to determine the effective number of hidden layer neurons. The learning efficiency of the algorithm was improved through the reduction of network complexity. Then, to improve the classification accuracy and generalization performance of the algorithm, a deep network structure was introduced to the KI-ELM to gradually extract the original input data layer by layer and realize high-dimensional mapping of data. The experimental results show that the number of network nodes of HI-DKIELM algorithm is obviously reduced, which reduces the network complexity of ELM and greatly improves the learning efficiency of the algorithm. From the regression and classification experiments, its CCPP can be seen that the training error and test error of the HI-DKIELM algorithm proposed in this paper are 0.0417 and 0.0435, which are 0.0103 and 0.0078 lower than the suboptimal algorithm, respectively. On the Boston Housing database, the average and standard deviation of this algorithm are 98.21 and 0.0038, which are 6.2 and 0.0003 higher than the suboptimal algorithm, respectively.

Application of Machine Learning in Boston House Price Prediction

House prices are an important economic indicator for a country or region, rising house prices are often associated with economic growth and increased employment opportunities, while a decline in house prices may indicate an economic slowdown or other unfavorable factors. Governments and relevant agencies need to understand the dynamics of housing prices to formulate appropriate housing policies and plan urban development. As one of the major economic centers in the United States, the fluctuations in Boston's housing prices can reflect the local economic conditions and development trends. This paper selects data from Boston in 1970. Each record in this database describes a Boston suburb or town. The proposed method was evaluated using 5 metrics. By comparison, the paper ends up with the result that XGBoost works best out of the four regression models.

A Design and Its Application of Multi-Granular Fuzzy Model with Hierarchical Tree Structures

This paper is concerned with the design of a context-based fuzzy C-means (CFCM)-based multi-granular fuzzy model (MGFM) with hierarchical tree structures. For this purpose, we propose three types of hierarchical tree structures (incremental, aggregated, and cascaded types) in the design of MGFM. In general, the conventional fuzzy inference system (FIS) has problems, such as time consumption and an exponential increase in the number of if–then rules when processing large-scale multivariate data. Meanwhile, the existing granular fuzzy model (GFM) reduces the number of rules that increase exponentially. However, the GFM not only has overlapping rules as the cluster centers become closer but also has problems that are difficult to interpret due to many input variables. To solve these problems, the CFCM-based MGFM can be designed as a smaller tree of interconnected GFMs. Here, the inputs of the high-level GFMs are taken from the output to the low-level GFMs. The hierarchical tree structure is more computationally efficient and easier to understand than a single GFM. Furthermore, since the output of the CFCM-based MGFM is a triangular fuzzy number, it is evaluated based on a performance measurement method suitable for the GFM. The prediction performance is analyzed from the automobile fuel consumption and Boston housing database to present the validity of the proposed approach. The experimental results demonstrate that the proposed CFCM-based MGFM based on the hierarchical tree structure creates a small number of meaningful rules and solves prediction-related problems by making them explainable.

Application of LADMM and As-LADMM for a High-Dimensional Partially Linear Model

This paper mainly studies the application of the linearized alternating direction method of multiplier (LADMM) and the accelerated symmetric linearized alternating direction method of multipliers (As-LADMM) for high dimensional partially linear models. First, we construct a l1-penalty for the least squares estimation of partially linear models under constrained contours. Next, we design the LADMM algorithm to solve the model, in which the linearization technique is introduced to linearize one of the subproblems to obtain an approximate solution. Furthermore, we add the appropriate acceleration techniques to form the As-LADMM algorithm and to solve the model. Then numerical simulations are conducted to compare and analyze the effectiveness of the algorithms. It indicates that the As-LADMM algorithm is better than the LADMM algorithm from the view of the mean squared error, the number of iterations and the running time of the algorithm. Finally, we apply them to the practical problem of predicting Boston housing price data analysis. This indicates that the loss between the predicted and actual values is relatively small, and the As-LADMM algorithm has a good prediction effect.

Twin extreme learning machine based on heteroskedastic Gaussian noise model and its application in short-term wind-speed forecasting

Extreme learning machine (ELM) has received increasingly more attention because of its high efficiency and ease of implementation. However, the existing ELM algorithms generally suffer from the drawbacks of noise sensitivity and poor robustness. Therefore, we combine the advantages of twin hyperplanes with the fast speed of ELM, and then introduce the characteristics of heteroscedastic Gaussian noise. In this paper, a new regressor is proposed, which is called twin extreme learning machine based on heteroskedastic Gaussian noise (TELM-HGN). In addition, the augmented Lagrange multiplier method is introduced to optimize and solve the presented model. Finally, a significant number of experiments were conducted on different data-sets including real wind-speed data, Boston housing price dataset and stock dataset. Experimental results show that the proposed algorithms not only inherits most of the merits of the original ELM, but also has more stable and reliable generalization performance and more accurate prediction results. These applications demonstrate the correctness and effectiveness of the proposed model.

Perspectives and recommendations of opioid overdose survivors experiencing unsheltered homelessness on housing, overdose, and substance use treatment in Boston, MA.

Opioid overdose causes one in four deaths among people experiencing homelessness in Boston, MA. To reduce overdose risks, the experience and perspectives of people experiencing homelessness should be incorporated into housing, overdose prevention, and substance use treatment efforts. In 2021, we conducted qualitative interviews with 59 opioid overdose survivors to inform equitable access to treatment services. In response to policy debate surrounding a public drug scene near a key recruitment site, we conducted a targeted thematic analysis of transcribed interview data from a subset of participants experiencing unsheltered homelessness (n=29) to explore their perspectives and recommendations on housing, overdose prevention, and substance use treatment. Among 29 participants who identified as non-Hispanic Black (n=10), Hispanic/Latinx (n=10), or as non-Hispanic White (n=9), the median number of self-reported opioid overdoses in the past three months was 2.0 (SD 3.7). Three themes emerged from this targeted analysis: (1) Participants described inadequate housing resources and unwelcoming shelter environments. (2) Participants near a large public drug scene explained how unsheltered homelessness was chaotic, dangerous, and disruptive to recovery goals. (3) Participants provided recommendations for improving housing and addiction treatment systems and including their perspectives in the development of solutions to the intersecting housing and opioid overdose crises. The overdose prevention, housing and substance use treatment systems must address the needs of opioid overdose survivors experiencing unsheltered homelessness. Overdose survivors experiencing unsheltered homelessness described a chaotic public drug scene but resorted to residing in nearby encampments because the existing shelter, housing, and addiction treatment systems were unwelcoming, difficult to navigate, or unaffordable. Despite efforts to provide low-threshold housing in Boston, additional low-barrier housing services (i.e., including harm reduction resources and without "sobriety" requirements) could promote the health and safety of people who use drugs and are experiencing homelessness.

Deriving Controllable Local Optimal Solutions through an Environment Parameter Fixed Algorithm

This paper addresses the challenge of optimizing objective functions in engineering problems influenced by multiple environmental factors, such as temperature and humidity. Traditional modeling approaches often struggle to capture the complexities of non-ideal situations. In this research, we propose a novel approach called the Environment Parameter Fixed Algorithm (EPFA) for optimizing the objective function of a deep neural network (DNN) trained in a specific environment. By fixing the environmental parameters in the DNN defined objective function, we transform the original optimization problem into a control parameter optimization problem. We integrate EPFA-CLS (Controllable local-Optimal Solution) with Gradient Descent and algorithms such as Adagrad to obtain the optimal solution. To demonstrate the concept, we apply our approach to an optimal course model and validate it using optimal course and Boston house price datasets. The results demonstrate the effectiveness of our approach in handling optimization problems in complex environments, offering promising outcomes for practical engineering applications.