# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 2 Literature Review and State of the Art
### Literature Review House Price Prediction  
Predicting house prices is a popular task in machine learning, and there are numerous state-of-the-art papers that use NNs and other machine learning techniques for this task.  
[PERSON] et al.'s work is one of the first research studies that demonstrated that the artificial neural network model has significant potential in predicting house prices. The purpose of this research is to evaluate and compare the effectiveness of the hedonic model and an artificial neural network model in predicting house prices. The study uses a sample of 200 houses in Christchurch, New Zealand, obtained randomly from the Harcourt website, taking into account factors such as house size, age, type, the number of bedrooms, bathrooms, garages, amenities, and location [16].  
[PERSON] et al. propose a hybrid model that combines deep learning recurrent neural networks (RNNs) and random forest algorithms for predicting housing prices. The model uses features extracted from the image and text data of the houses, along with other relevant numerical data. The results show that the proposed model outperforms both standalone deep learning and random forest models for the prediction of housing prices in Brazil [17].  
[PERSON] et al. propose a novel approach for estimating housing prices using a combination of interior and exterior images of houses, as well as satellite images. The proposed method utilizes deep convolutional neural networks to extract features from images and then (along with other house features) employs multiple regression models to estimate house prices based on these features [18].  
[PERSON] et al. present a study on the use of machine learning algorithms for housing price prediction in California during the COVID-19 pandemic. The authors analyzed different regression models, including linear regression, decision trees, and others to predict the price of houses in different regions of California. The study also evaluated the impact of the pandemic on the housing market and the performance of the different models [7].  
[PERSON] et al. explore the application of various machine learning algorithms, including support vector machine, random forest, XGBoost, LightGBM, and CatBoost, as automatic real-estate valuation models. The study involves analyzing approximately 57,000 records on apartment transactions in Seoul in 2018 and proposes combination methods to improve the models' predictive power. The findings suggest that machine learning predictors outperform conventional models, and an efficient averaging of the predictors can improve their predictive accuracy. Additionally, machine learning algorithms can recommend which algorithm should be selected for making predictions [19].  
[PERSON] et al. propose to use multiple different models that can be used for prediction and focus on more accurate results. They propose to use an ensemble learning method and combine multiple ML models to improve the results [20].  
[PERSON] et al. propose a hybrid Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) model for real-estate price prediction. The model combines the strengths of both GRU and LSTM networks. The results of these experiments in the Boston dataset showed that the proposed model has better performance when the networks are used in the fusion process than when they act individually [21].  
[PERSON] et al. investigate the dynamics of the real-estate market in Kyiv, Ukraine, focusing on the impact of various economic factors on housing prices. The study employs a dual-approach forecasting model, combining traditional methods with advanced analytical tools to enhance prediction accuracy. Ultimately, the research aims to provide a robust framework for future forecasting in the volatile real-estate sector [22].