house price prediction presentation

How to Build A House Price Prediction Model – Linear Regression Explained

Ever wondered how algorithms predict future house prices, stock market trends, or even your next movie preference? The answer lies in a fundamental yet powerful tool called linear regression.

Don't be fooled by its seemingly simple equation – this article will unveil its magic, empowering you to build and understand these models, whether you're a machine learning newcomer or a seasoned expert who needs a refresher.

In this article you will get crystal clear explanations, hands-on guidance, and real world applications where you will witness the power of linear regression in action.

So, buckle up and get ready to conquer the straight and narrow path of linear regression! By the end of this comprehensive guide, you'll be equipped to confidently build, interpret, and leverage these models for your own data-driven endeavors.

Table of Contents

Prerequisites, what is linear regression, why is linear regression valuable, linear regression key concepts, how to build your first model, advanced linear regression techniques.

Before we begin, make sure you have the following installed:

• Python (3.x recommended)
• Jupyter Notebook : this is optional but recommended for an interactive environment and also for trial and error (beginners will benefit the most from this)
• Required libraries: pandas, NumPy, Matplotlib, seaborn, scikit-learn

You can install these libraries using pip install in the command line:

In simple terms, Linear Regression harnesses the power of straight lines.

Imagine you are a realtor trying to predict the price of a house. You might consider various factors like its size, location, age and number of bedrooms.

Linear regression comes in as a powerful tool to analyze these factors and unveil the underlying relationships. It is a powerful statistical technique that allows us to examine the relationship between one or more “independent” variables and a “dependent” variable.

In a house price dataset, the independent variables are columns used to predict, such as the “Area”, “Bedrooms”, “Age”,  and “Location”. The Dependent variable will be the “Price” column – the feature to be predicted.

Linear regression is the simplest form of regression, assuming a linear (straight line) relationship between the input and the output variable. The equation for simple linear regression can be expressed as y = mx + b , where, y is the dependent variable, x is the independent variable, m is the slope, and b is the intercept.

This creates a best fit line, but do not worry too much about the underlying math. However, it is important as you go further in your machine learning journey.

• Interpretability: Unlike some complex models, linear regression provides clear insights into how each feature influences the target variable. You can easily see which features have the strongest impact and how they contribute to the overall prediction.
• Baseline for complex models: When dealing with more intricate problems, data scientists often start with linear regression as a baseline model. This simple model serves as a reference point to compare the performance of more complex algorithms. If a complex model doesn't offer significantly better results than linear regression, it might be unnecessarily overfitting the data.
• Ease of implementation: Compared to other machine learning algorithms, linear regression is relatively easy to implement and understand. This makes it a great starting point for beginners venturing into the world of machine learning.

Remember, linear regression might not be the ultimate solution for every problem, but it offers a powerful foundation for understanding data, building predictions, and setting the stage for exploring more complex models.

Let us dive deeper into the mechanics of building and interpreting linear regression models.

Ready to dive deeper into the mechanics of linear regression? Don't worry, even without a PhD in math, we can unlock its secrets together without getting bogged down in the complicated terms.

What happens when your create a Linear Regression Model?

• Find the best fit line: Lines are drawn across a graph, with features on one axis and prices on the other. The line we're looking for is the one that best fits the dots representing real houses, minimizing the overall difference between predicted and actual prices.
• Minimize the error: Think of the line as a balancing act . The line's slope and position is adjusted until the total distance between the line and the data points is as small as possible (Minimized Cost Function). This minimized distance reflects the best possible prediction for new houses based on their features.
• Coefficients: Each feature in the model gets a weight (Coefficients), like a specific amount of an ingredient in a recipe. By adjusting these weights, we change how much each feature contributes to the predicted price. A higher weight for size, for example, means that larger houses tend to have a stronger influence on the predicted price.

So, what do we get out of this?

Once we have the best-fit line, the model can predict the price of new houses based on their features. But it's not just about the numbers – the weights tell a story .

They reveal how much, on average, each feature changes the predicted price. A positive weight for bedrooms means that, generally, houses with more bedrooms are predicted to be more expensive.

Assumptions and Limitations

Linear regression assumes things are roughly straightforward, like the relationship between size and price. If things are more complex, it might not be the best tool.

But it's a great starting point because it's easy to understand and interpret, making it a valuable tool to explore the world of data prediction.

However, you do not have to worry about finding the best fit line manually. The algorithm picks the best fit line when creating the model.

In the next section, you will learn how to build your very first House Price Prediction model.

How to import libraries and load data

If you are new to machine learning models, the libraries are imported as abbreviations for the sole purpose of writing shorter code:

The dataset is loaded using pandas’ read_csv function and then the first five rows are displayed using df.head() .

Exploratory Data Analysis (EDA)

Data from different sources are usually messy, scattered, they contain missing values, and are sometimes unstructured.

Before building a regression model, it's crucial to understand the data, and clean and optimize it for the best result. For an in-depth explanation check out this article on data cleaning and preprocessing .

Let's go over the steps you should take before building your model.

Check for missing values

Machine learning models cannot function when there are missing values in the dataset:

This will give you a list of columns that have null values and the rows themselves. There are different ways to deal with this such as:

• Deleting all rows with null values.
• Using the mean or median of the column to fill in the missing values for numerical data.
• Filling the missing values with the most occurring data for qualitative data.

Explore the correlation between variables

This code will show the relationships between the columns of independent / variables / features, and dependent/ target variables.

It will also show which columns or features determine the outcome of the target variable more than others.

Visualize the relationship between independent and dependent variables

Scatter plots can show how well your predicted prices align with actual values. Residual plots help visualize any patterns in the errors, revealing potential issues.

This scatter plot shows the relationship between independent and dependent variables and a straight line is drawn to show the relationship

Data preprocessing

This is a crucial step as the quality of data that is used to train the model also determines  the accuracy and efficiency of the model.

Here, the data set is first separated into X (independent variable(s)/ features) and Y (dependent variable/ Target):

We handle the missing values by dropping columns with missing/ null values and split the dataset into training and testing in a 80:20 ratio.

Building the Regression Model

Finally , it is time to create and train our linear regression model.

We create a model by calling an instance of the model into a variable as shown below and train the model by fitting the training dataset into the model.

How to make Predictions

The trained model is used to make predictions on the test set. Predictions can be made on the entire feature column as shown below or each column can be predicted individually.

How to evaluate the Model

Evaluating the model's performance is an important step to determine the accuracy of the model and reusability. We can check using metrics such as:

• “R-squared”: This tells you how well the model explains the variation in house prices. A higher value (closer to 1) indicates a better fit.
• Mean Squared Error (MSE): This measures the average difference between predicted and actual prices. Lower is better.
• Precision score.

How to visualize the results

Visualize the regression line and actual vs. predicted values:

Let's consider a different scenario where you want to flex your muscles and predict a student's score (y) based on the number of hours they studied (x). The linear regression model might look like this:

You've conquered the basics of linear regression, but the journey continues!

Let's explore advanced techniques to unlock even more power and fine-tune your models.

Taming overfitting – Regularization

Imagine a cake smothered in frosting – impressive, but impractical.

Similarly, a model with too many features can "overfit" the training data, losing its ability to generalize. Regularization techniques act like seasoning, preventing this culinary catastrophe:

• L1 (Lasso): Shrinks some coefficients to zero, effectively removing unimportant features.
• L2 (Ridge): Shrinks all coefficients, preventing them from becoming too large.

These techniques penalize complex models, pushing them towards simpler solutions that generalize better to new data.

Feature engineering – Unearthing hidden gems

Not all features are created equal. Some might be redundant, while others hide valuable relationships. Feature engineering involves:

• Selection: Identifying the most informative features using correlation analysis or statistical tests.
• Transformation: Creating new features by combining existing ones (for example, multiplying square footage and bedrooms for total living area). This allows you to capture non-linear relationships beyond the linear model's capabilities.

By carefully selecting and transforming features, you can significantly boost your model's performance.

Categorical quandaries – Encoding and beyond

The world isn't always black and white. What about features like "city" or "property type"? These categorical variables require special handling:

• One-hot encoding: Creates separate binary features for each category, allowing the model to learn their individual impact.
• Polynomial features: Creates new features by interacting categories (for example, "city * property type"), capturing complex relationships.

Understanding how to handle categorical features unlocks valuable insights from your data. Check out this article for a deep dive on how to handle categorical features.

Model Selection – How to choose Your Champion

With this arsenal of techniques, you might have multiple models. How do you pick the best one? Consider:

• Complexity: Simpler models are generally preferred, as they are less prone to overfitting.
• Performance: Metrics like R-squared and cross-validation help compare models objectively.

Finding the right balance between complexity and performance is crucial for building effective and generalizable models.

Remember!  Mastering linear regression is an ongoing journey. Experiment, explore these advanced techniques, and don't be afraid to get creative! With practice and curiosity, you'll unlock the true potential of this powerful tool and extract valuable insights from your data.

For further exploration check out the documentation:

• Scikit-learn documentation
• TensorFlow tutorials

This exploration of linear regression has equipped you with a robust understanding of its core concepts, model building process, and limitations.

Remember, this is merely the foundation. As you venture deeper, you'll encounter advanced techniques like regularization, feature engineering, and handling categorical features, unlocking even greater predictive power.

Embrace the spirit of exploration. Experiment, delve into the resources provided, and remember that mastering linear regression is an ongoing journey. Each challenge overcome, each model built, strengthens your ability to extract valuable insights from data.

So, continue learning, keep building, and unlock the true potential of this powerful tool.

If you found this helpful connect with me on LinkedIn .

I am a Data scientist, Machine learning engineer and Bioinformatician who is passionate about teaching and tutoring and I would love to share this passion with the world

If this article was helpful, share it .

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housing-price-prediction

Here are 22 public repositories matching this topic..., viveckh / lilhomie.

A Machine Learning Project implemented from scratch which involves web scraping, data engineering, exploratory data analysis and machine learning to predict housing prices in New York Tri-State Area.

• Updated Dec 8, 2022
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bibek376 / Housing_Price_Prediction

• Updated Dec 3, 2021

tatha04 / Housing-Prices-Advanced-Regression-Techniques

This notebook explores the housing dataset from Kaggle to predict Sales Prices of housing using advanced regression techniques such as feature engineering and gradient boosting.

• Updated Jun 3, 2021

SouravG / Housing-price-prediction-using-Regularised-linear-regression

Housing price prediction using Regularised linear regression

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vaibhavvikas / housing-price-predictor

A machine learning project to predict the housing price based on Kaggle Housing Prices Competition

• Updated Oct 17, 2022

sbera7 / Bengaluru-housing-price

Predicting housing prices using Bengaluru house price dataset

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saikatXshrey / college-major-project

Search places get housing price🤑

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showman-sharma / Housing-Price-Analysis

We are required to build a regression model using regularisation in order to predict the actual value of the prospective properties and decide whether to invest in them or not. The company wants to know: 1. Which variables are significant in predicting the price of a house, and 2. How well those variables describe the price of a house.

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jsutch / TensorFlow

TensorFlow/Keras examples and notes.

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Prediction of "Dragon Real State" will be here!!!

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sauravwel / Boston-Housing-Price-Prediction-using-Deep-Learning

This project uses deep learning techniques to predict median housing prices in the Boston area using the Boston Housing dataset. The model employs TensorFlow, Keras, and Numpy, with a mean squared error loss function and Adam optimization algorithm. The results show high accuracy.

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Ahamad76 / Housing-Price-Prediction

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annareddy1 / King-County-Housing-Price-Prediction-Web-App

This project enables figuring out the key features that determine the sales price of houses. The resulting Web App helps real estate developers, individual buyers, and banks seek the best area in King County to develop new apartment buildings or make purchases.

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vishal017 / Melbourne_Housing

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annareddy1 / Boston-Housing-Analysis

Provided valuable insights into the predictive performance of different modeling methodologies for housing price prediction in Boston. It suggests that a combination of linear and non-linear models can be effective and lays the foundation for further research and practical applications in this domain.

nicolasloontjens / kaggle-house-prices-competition

My entry for the house prices competition, with a Kaggle score of 0.15537 using elastic net

• Updated Jul 7, 2022

Abdelrahman-Amen / Housing-Price

Predicting housing prices with machine learning regression models. This project implements Linear Regression, Random Forest, and Decision Tree models for accurate predictions.

• Updated Jan 22, 2024

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BST 260 Final Project: Predicting House Price

The website of BST260 final project- House Price Prediction

BST260 Final Project: House Price Prediction

Watch Our Video on YouTube!

Overview and motivation.

(For this demo visualization, data was provided by Redfin , a national real estate brokerage)

Growing unaffordability of housing has become one of the major challenges for metropolitan cities around the world. In order to gain a better understanding of the commercialized housing market we are currently facing, we want to figure out what are the top influential factors of the housing price. Apart from the more obvious driving forces such as the inflation and the scarcity of land, there are also a number of variables that are worth looking into. Therefore, we choose to study the house prices predicting problem on Kaggle, which enables us to dig into the variables in depth and to provide a model that could more accurately estimate home prices. In this way, people could make better decisions when it comes to home investment.

Our object is to discuss the major factors that affect housing price and make precise predictions for it. We use 79 explanatory variables including almost every aspect of residential homes in Ames, Iowa. Methods of both statistical regression models and machine learning regression models are applied and further compared according to their performance to better estimate the final price of each house. The model provides price prediction based on similar comparables of people’s dream houses, which allows both buyers and sellers to better negotiate home prices according to market trend.

Related Work

Here is an outline of our related work for the project.

Exploratory Data Analysis

Data wrangling and cleaning

Regression, Stepwise Model Selection

Principal Component Analysis

• Lasso Regression
• Random Forest
• Gradient Boosting

Ensemble Learning

Model Evaluation and Final Analysis

Initial Questions

Through this project, we sought to answer some major questions:

What are the important features that affect the house price?

How to build a model to predict the house price?

How to evaluate our prediction performance?

It is our job to predict the sales price for each house. For each Id in the test set, we must predict the value of the SalePrice variable.

The metric to evaluate the models is Root-Mean-Square-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price. Our predictions are evaluated on Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted value and the logarithm of the observed sales price.

Our data was obtained from Ames Housing dataset , which was compiled by Dean De Cock for use in data science education. It’s an incredible alternative for data scientists looking for a modernized and expanded version of the often cited Boston Housing dataset. The data includes 79 explanatory variables describing (almost) every aspect of residential homes.

We also participated in the Kaggle Competition House Prices: Advanced Regression Techniques .Our best entry for the competition is 0.1169, which leads us to 367/2636 (top 15%) in the leaderboard!

All the codes and procedures of this project can be found at our full project repository.

Firstly, we do some EDAs to gain a general understanding of our data, and detecting some important matrices and trends that may come helpful for our further analysis and model building.

• SalesPrice vs. Living Area

From the plot above we can see that there are two outliers which has high areas but low sale price. When fitting the models, we delete these two outliers in the training data.

• SalesPrice vs. Overall Quality

Conincided with our intuition, if the overall quality of the house is better, then the house price is higher.

• SalesPrice vs. Year Bulit

• SalesPrice vs. Year Remodel

In general, the newer the house is, the higher the price is. But the correlation is not very strong.

• Correlation Matrix

Here we examined the correlations between variables and correlations with our outcome of interest: SalePrice.

correlations between variables:

Correlations with SalePrice: Here we use the R package tabplots to find strong-related variables to “Saleprice” among 79 variables, which would further help us do feature selection and engineering.

Here are some of the plots we generated with R package tabplots to show the number and range of values for each variable as well as the covariance among the variables:

Data Cleaning and Feature Engineering

Before we rush into regression and machine learning prediction, it is very important to get our data “cleaned” enough. This process usually take 80% of time in a real-world data problem. In fact, in our project, we spend about 60% of our time cleaning the data ourselves!

• Missing Data and Different Data Types

When using the data, we must be careful about the following variables:

Ordinal feature: ExterCond, ExterQual, Fence, FireplaceQu, Functional, GarageFinish, GarageQual, HeatingQC, KitchenQual, OverallCond, OverallQual, BsmtCond, BsmtQual, BsmtExposure, BsmtFinType1, BsmtFinType2, GarageCond, PavedDrive

Read as numerial but actually is categorical: MoSold, MSSubClass

• Filling NAs and scale the data

There are, indeed, a lot of NAs in our oringinal dataset, which we need to clean the dataset and fill in the NA with appropriate value to make our prediction. Then, we try to fill the NAs by using their properties according to the value in those columns.

First, for the columns contain large percentage of NAs, we may remove the columns or combine them with other columns and we fill in the missing value with “none”.

PoolQC(2909/2919=99.66%) MiscFeature(2814/2919=96.4%) Alley(2721/2919=93.22%)

Then, we will deal with the other columns which contain NAs by replacing the missing values according to the strong correlation within those columns and determine the value we should fill in.

1.For columns like Fence, FireplaceQu, BsmtCond, BsmtExposure, BsmtFinType2, BsmtFinType1, BsmtFinSF2 and BsmtQual, the NA is meaningful, thus, we see these NAs as no fence, no fireplace, no Basement, and when we transform these categorical data into numeric ones, we set these NAs as 0.

2.GarageQual and GarageCond, which highly correlated, we keep GarageQual and remove GarageCond, and then transform into numeric data

3.GarageYrBlt:159 NAs, except one outlier(2207), which we deal with a typo, and change that to ‘2007’(“YearBuilt” = 2006); others, we saw those as the very original ones which maybe built earlier than the data can be reached, Min-1 = 1894

4.For Exterior1st and Exterior2nd, which only contain 1 missing value and there aren’t any other features that can help us determine what value we should fill in the NAs, therefore, we replace NAs in these two columns with ‘Other’.

5.In the columns that with only few missing values, we can replace the missing value with median, mean or mode value from each column.

Third, after filling in the missing value of our original dataset, we can also combine some columns and transform ordinal feature into numeric to make better prediction, which can solve our initial objectives of our project.

1.combine bath= full bath + half bath

2.Transform ordinal feature, such as ExterCond, ExterQual, Functional, GarageFinish, GarageQual, HeatingQC, KitchenQual, OverallCond, OverallQual, BsmtCond, BsmtQual, BsmtExposure, BsmtFinType1, BsmtFinType2, GarageCond, PavedDrive to numeric data

3.Also, some columns in our dataset such as MoSold and MSSubClassRead, they are read as numerical but actually are categorical.

Finally, we use one-hot method to deal with categorical features excluding ordinal ones. For example, there are two possible values - “Grvl” and “Pave” - for the feature “Street”. In the new dataframe, we create two new columns “Street_Grvl” and “Street_Pave”, and delete the old column “Street”. If the observation’s value for “Street” is “Grvl” in the old dataframe, then in the new dataframe “Street_Grvl” is set to 1 and “Street_Pave” is set to 0.

• Model Selections

Stepwise Selection combines elements of both forward selection and backward elimination, allow us either to remove covariates from our previous model or add back in covariates that we had previously eliminated from our model, and in this sense, giving us chances to consider all possible subsets of the pool of explanatory variables and find the model that best fits the data according to some prespecified criterion, such as AIC(Akaike Information Criterion), BIC(Bayesian Information Criterion), and adjusted R square.[]

• Lowess Anlaysis

LOWESS (Locally Weighted Scatterplot Smoothing), or LOESS (Locally Weighted Smoothing), is often applied in regression analysis that creates a smooth line through a scatter plot. It is especially helpful when detecting nonlinear relationship between variables and predicting trends. In our study, LOWESS was first used to detect potential nonlinear associations between variables and sale prices. Since it performed the best results compared to other smoothing methods, we then used it to predict prices after PCA preprocessing.

• Principal component analysis

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

We also run PCA on our data sets. PC1 and PC2 represent 1st principal component and 2ed principal component. Top 50 features with the most meaningful effects were chosen in the plot, where distances between covariates suggest their degrees of correlation. We can see some patterns in the projection plot: garage condition is correlated with garage quality; fireplace is correlated with wood deck area.

As mentioned in class, we can’t estimate the estimators of a high-dimensional nonlinear model via lm function. So we applied PCA to estimate predictors by minimizing the squared error of the approximation.

Lasso (Least Absolute Shrinkage and Selection Operator) regression is a regularized linear regression. It uses L1 norm to constrain the coefficients of the fitting model. Usually, some coefficients will be set to 0 under the constrain. Therefore, the lasso regression is more robust compared to ordinary linear regression.

Machine Learning

Random forest is an ensembling machine learning method basing on classification tree or regression tree. In general, random forest will generate many decision trees and average their predictions to make the final prediction. When generating each decision tree, the random forest will use a subset of all features, which avoids the overfitting problem.

• Regression Tree

To our surprise, the overall quality of the house is more important than the total square feet. The year when the house was built or remodeled also plays an important role in pricing. This coincide with our intuition since the year is related to the quality.

• Ensemble Methods

Ensemble learning combines multiple statistical and machine learning algorithms together to achieve better predictive performance than any algorithm alone, because the errors in each model may cancel out in the ensembled model. In our project, we will try to ensemble the regression techniques we use (e.g. lasso regression, gradient boosting), to predict the sale prices and compare the ensembled model with other models.

In our project, we just simply stack several models, i.e. average their predictions to make our final prediction.

Final Analysis

Our goal is to minimize the RMSE after log transformation, so when training the model, the target value is the logarithm of the observed sales price. Besides, we add one more feature - total square feet “TotalSF”, which is defined as TotalSF = TotalBsmtSF + 1stFlrSF + 2ndFlrSF.

Some models (e.g. linear models) perform better when the predictors are “normal”. Therefore we use Box-Cox transformation to transform the features of which skewness is high.

• Some Important Matrices

In our model, we can easily find that housing price realted a lot of factors, some of these factors are listed in the circle above. To be specific, overall quality increase 1, the house price would increase 8762, and when GrLiveArea(Above grade (ground) living area square feet) increase 1, the housing price would increase a lot, which can be 58249; on the other hand, when the house near the rail road, the housing price will decrease 11403, these all meet our intuitive knowledge. We have a little visualization of those important matrices as follows:

• Model Evaluations

We use 5-fold cross validation to evaluate how each model performs. Each model’s RMSEs in cross validation (CV) and in leaderboard (LB) are as follows:

Alough lasso performs best in cross validation, but gradient boosting model provided by sk-learn is better in leader board. We think that it comes from the overfitting problem of lasso regression. In both cross validation and leaderboard, the random forest does not perform well. In this test, random forest avoid the problem of overfitting, but it underfits the data at the same time. The “PCA + LOESS” model performs worst, since LOESS model is not a good model for complex regression problem.

• Ensemble Methods and Kaggle

Based on the above result, we choose two models - lasso and gradient boosting in sklearn, and average their predictions to make our final prediction. The RMSE of the stacking model is 0.1169, which leads us to 367/2636 (top 15%) in the leaderboard.

Code can be found on: https://github.com/BST260-final-group-project/project-files/tree/master/final-analysis

• The Source of Data is from Ames Housing dataset
• The Github page of our full project repository. All the codes and procedures can be found here

Effective House Price Prediction Using Machine Learning

• Conference paper
• First Online: 24 September 2023
• Cite this conference paper

• Jincheng Zhou   ORCID: orcid.org/0000-0002-1995-4002 12 ,
• Tao Hai   ORCID: orcid.org/0000-0002-6156-1974 12 , 14 ,
• Ezinne C. Maxwell-Chigozie   ORCID: orcid.org/0000-0002-0422-1359 13 ,
• Afolake Adedayo   ORCID: orcid.org/0000-0002-4057-3861 13 ,
• Ying Chen 14 ,
• Celestine Iwendi   ORCID: orcid.org/0000-0003-4350-3911 13 &
• Zakaria Boulouard   ORCID: orcid.org/0000-0002-4891-3760 15  

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 735))

Included in the following conference series:

• International Conference on Advances in Communication Technology and Computer Engineering

In recent times, there have been a surge in the housing business, such that prediction of houses is of utmost important both for the seller and the potential buyer. This has been influenced by several key indices. Many approaches have been used to tackle the issue of predicting house prices to help the house owners and real estate agents maximise their profit while the prospective buyers make better informed decision. This study focuses on building an effective model for the prediction of house prices. Since price is a continuous variable, it was expedient we used regression models. Some regression models like linear regression, Random Forest regressor (RF), Extreme Gradient Boosting Regressor (XGBoost), Support Vector Machine (SVM) regressor, K-Nearest Neighbor (KNN) and Linear regression were employed. The result showed that Random Forest Regressor showed a superior performance having an R2 score of 99.97% while SVM regressor performed poorly with an R2 score of −4.11%. The result proved that Random Forest regressor as an effective machine learning model to predicting house prices.

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Acknowledgements

This work was supported by Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education of Guizhou University (GZUAMT2022KF[07]), the National Natural Science Foundation of China (No.61862051), the Science and Technology Foundation of Guizhou Province (No.[2019]1299, No.ZK[2022]449), the Top-notch Talent Program of Guizhou province (No.KY[2018]080), the Natural Science Foundation of Education of Guizhou province(No.[2019]203) and the Funds of Qiannan Normal University for Nationalities (No. qnsy2019rc09). The Educational Department of Guizhou under Grant NO. KY[2019]067.

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Jincheng Zhou & Tao Hai

School of Creative Technologies, University of Bolton, Bolton, BL3 5AB, UK

Ezinne C. Maxwell-Chigozie, Afolake Adedayo & Celestine Iwendi

School of Information and Artificial Intelligence, Nanchang Institute of Science and Technology, Nanchang, China

Tao Hai & Ying Chen

LIM, Hassan II University of Casablanca, Casablanca, Morocco

Zakaria Boulouard

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Hassan II University Casablanca, El Mansouria, Morocco

Department of Information Systems, Comenius University, Bratislava, Slovakia

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Zhou, J. et al. (2023). Effective House Price Prediction Using Machine Learning. In: Iwendi, C., Boulouard, Z., Kryvinska, N. (eds) Proceedings of ICACTCE'23 — The International Conference on Advances in Communication Technology and Computer Engineering. ICACTCE 2023. Lecture Notes in Networks and Systems, vol 735. Springer, Cham. https://doi.org/10.1007/978-3-031-37164-6_32

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The Fed's inflation-fighting tactic was effective in cooling the housing market. What now?

The year started out with signs showing that the Federal Reserve’s inflation-fighting tactic was effective in cooling down the hot pandemic housing market.

For the first time in 11 years, home prices dropped year-over-year in February as mortgage rates more than doubled following the Fed’s consecutive interest rate hikes, curbing affordability.

However, the median price of a home increased month-over-month for the second consecutive month in March. The median home price is projected to increase for a third month in a row in April to $393,300, which is 2% lower than the previous April’s median price of $401,700, according to data released in May by the National Association of Realtors (NAR).

One big factor behind the strengthening home prices and the decrease in sales volume -- down 23% in April from a year ago -- is the lack of housing inventory.

“Home sales are bouncing back and forth but remain above recent cyclical lows,” says NAR Chief Economist Lawrence Yun. “The combination of job gains, limited inventory and fluctuating mortgage rates over the last several months have created an environment of push-pull housing demand.”

Learn more: Best personal loans

Housing: Is the housing market going to crash? What experts say about the possibility in 2023.

New construction: The great housing inventory divide: New construction vs existing-homes

Where are home prices headed?

Generally speaking, high mortgage rates should prompt house prices to trend downward.

“Yet, housing supply remains so restricted, that any uptick in demand will put upward pressure on prices,” wrote First American Chief economist Mark Fleming in a blogpost. “This is the dynamic that played out in March, as the spring home-buying season ushered in more demand for homes, while insufficient supply prompted buyers to compete and bid up prices.”

Affordability trends in the coming months will depend on mortgage rates and the supply and demand dynamics fueling nominal house price appreciation – dynamics that will play out differently in each market.

“Real estate affordability is local, too,” says Fleming.With median home prices for both existing and new homes above $380,000, and elevated mortgage rates, home buyers are in a tough spot, says Jeff Taylor, founder, Mphasis Digital Risk.

"With 5% down and 7% rates and $600 in non-housing debt, borrowers qualify for these purchase prices if they make above $100,000," Taylor told USA TODAY. "We've seen that buyers are more likely to act when income required to qualify drops below $100k. We need rates in the lower-6% range for this to happen."Taylor expects rates to drop and for inflation to wane this fall.

"So homebuyers shouldn't be discouraged," he says. "Instead, they should get pre-approved by lenders now, and those pre-approvals will only look more attractive as rates drop."

No return to typical seasonality in the market

There will be a lot of uncertainty in the economy over the next few months and prospective home buyers are going to be more opportunistic, as opposed to following traditional seasonal market trends, says Bright MLS Chief Economist, Lisa Sturtevant.

“There will continue to be volatility in mortgage rates as we wait to see what the Fed will do at its upcoming meetings and as we watch economic data roll in over the summer,” says Sturtevant. “Prospective buyers are going to be watching rates closely, and many will try to make an offer on a home when they see rates dip. As a result, we should expect less seasonality this year than we had prior to the pandemic.”

 More sellers returning to the market

While inventory will remain low this year, we should expect to see more sellers who had been on the sidelines list their home for sale this summer and into the fall, says Sturtevant.

Many existing homeowners have been “locked in” with super low mortgage rates, which has discouraged discretionary moves.

“However, some people have to move, and others will decide to move for a bigger or smaller home, or to change jobs or neighborhoods, despite rates remaining elevated,” says Sturtevant.

The uptick in new home construction has provided more opportunities for move-up buyers who may have been staying in place because they did not have anywhere to move to.

“One thing that could shut down new listings is if we see a sharp spike in mortgage rates to 8 or 9%, a situation that is still unlikely but not out of the realm of possibilities,” she says.

New home construction

Instability of regional banks is a concern for builder and land developer financing going forward, says Robert Dietz, chief economist for the National Association of Home Builders.

Lending conditions for builders have tightened, and the interest rate for development and construction loans is now well above 10%, which threatens housing supply.

Single-family spec home building loans had an effective rate of 13% in the first quarter of 2023 compared to 9% in the first quarter of 2018.

“Our expectation is that the rate of these loans will move lower as the Fed cuts the federal funds rate, but our forecast is that will not happen until later in 2024,” Dietz told USA TODAY. “As a result, land development would be suppressed, and we risk loaning low on lots during a home building rebound in 2024. Lot development can take three years in a typical market.”

Swapna Venugopal Ramaswamy is a housing and economy correspondent for USA TODAY.  You can follow her on Twitter @SwapnaVenugopal  and sign up for our Daily Money newsletter  here.

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We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on.

House Price Prediction using Machine Learning

So to deal with this kind of issues Today we will be preparing a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset. 

You can download the dataset from this link.

The dataset contains 13 features :

Importing Libraries and Dataset

Here we are using 

• Pandas – To load the Dataframe
• Matplotlib – To visualize the data features i.e. barplot
• Seaborn – To see the correlation between features using heatmap

As we have imported the data. So shape method will show us the dimension of the dataset. 

Output: 

Data Preprocessing

Now, we categorize the features depending on their datatype (int, float, object) and then calculate the number of them. 

Exploratory Data Analysis

EDA refers to the deep analysis of data so as to discover different patterns and spot anomalies. Before making inferences from data it is essential to examine all your variables.

So here let’s make a heatmap using seaborn library.

To analyze the different categorical features. Let’s draw the barplot .

The plot shows that Exterior1st has around 16 unique categories and other features have around  6 unique categories. To findout the actual count of each category we can plot the bargraph of each four features separately.

Data Cleaning

Data Cleaning is the way to improvise the data or remove incorrect, corrupted or irrelevant data.

As in our dataset, there are some columns that are not important and irrelevant for the model training. So, we can drop that column before training. There are 2 approaches to dealing with empty/null values

• We can easily delete the column/row (if the feature or record is not much important).
• Filling the empty slots with mean/mode/0/NA/etc. (depending on the dataset requirement).

As Id Column will not be participating in any prediction. So we can Drop it.

Replacing SalePrice empty values with their mean values to make the data distribution symmetric.

Drop records with null values (as the empty records are very less).

Checking features which have null values in the new dataframe (if there are still any).

OneHotEncoder – For Label categorical features

One hot Encoding is the best way to convert categorical data into binary vectors. This maps the values to integer values. By using OneHotEncoder , we can easily convert object data into int. So for that, firstly we have to collect all the features which have the object datatype. To do so, we will make a loop.

Then once we have a list of all the features. We can apply OneHotEncoding to the whole list.

Splitting Dataset into Training and Testing

X and Y splitting (i.e. Y is the SalePrice column and the rest of the other columns are X)

Model and Accuracy

As we have to train the model to determine the continuous values, so we will be using these regression models.

• SVM-Support Vector Machine
• Random Forest Regressor
• Linear Regressor

And To calculate loss we will be using the mean_absolute_percentage_error module. It can easily be imported by using sklearn library. The formula for Mean Absolute Error : 

SVM – Support vector Machine

SVM can be used for both regression and classification model. It finds the hyperplane in the n-dimensional plane. To read more about svm refer this.

Output : 

Random Forest Regression

Random Forest is an ensemble technique that uses multiple of decision trees and can be used for both regression and classification tasks. To read more about random forests refer this.

Linear Regression

Linear Regression predicts the final output-dependent value based on the given independent features. Like, here we have to predict SalePrice depending on features like MSSubClass, YearBuilt, BldgType, Exterior1st etc. To read more about Linear Regression refer this.

CatBoost Classifier

CatBoost is a machine learning algorithm implemented by Yandex and is open-source. It is simple to interface with deep learning frameworks such as Apple’s Core ML and Google’s TensorFlow. Performance, ease-of-use, and robustness are the main advantages of the CatBoost library. To read more about CatBoost refer this .

Clearly, SVM model is giving better accuracy as the mean absolute error is the least among all the other regressor models i.e. 0.18 approx. To get much better results ensemble learning techniques like Bagging and Boosting can also be used.

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