Essays about: "Predicting House Prices"

Showing result 1 - 5 of 11 essays containing the words Predicting House Prices.

1. 1. Enhancing House Rental Price Prediction Models for the Swedish Market : Exploring External features, Prediction intervals and Uncertainty Management in Predicting House Rental Prices

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Vasigaran Senthilkumar; [2023]
   Keywords : ;

   Abstract : Exakt förutsägelse av hyrespriserna för hus är ett avgörande problem i verkligheten fastighetsdomän, vilket underlättar informerat beslutsfattande för både hyresgäster och hyresvärdar. Denna studie presenterar en omfattande utforskning av olika maskininlärningstekniker som tillämpas på en mångsidig datauppsättning av husfunktioner, med det övergripande målet att avslöja den mest effektiva algoritmen för förutsäga hyrespriser. READ MORE

3. 2. An Evaluation of Leading Indicators in the Context of a Swedish Recession

   University essay from Lunds universitet/Nationalekonomiska institutionen

   Author : Rami Soliman; [2023]
   Keywords : Probit; Financial Crisis; Recession; Sweden; Leading Indicators; Business and Economics;

   Abstract : The aim of this paper is to evaluate potential leading indicators of a recession in Sweden. To answer the question potential leading indicators are first identified with previous findings in literature and with the current state of the Swedish financial system as background. READ MORE

4. 3. Improving House Price Prediction Models: Exploring the Impact of Macroeconomic Features

   University essay from Uppsala universitet/Statistiska institutionen

   Author : Martin Holmqvist; Max Hansson; [2023]
   Keywords : Machine learning; random forest; xgboost; macroeconomic features; house prices;

   Abstract : This thesis investigates if house price prediction models perform better when adding macroe- conomic features to a data set with only house-specific features. Previous research has shown that tree-based models perform well when predicting house prices, especially the algorithms random forest and XGBoost. READ MORE

5. 4. Predicting House Prices on the Countryside using Boosted Decision Trees

   University essay from KTH/Matematisk statistik

   Author : War Revend; [2020]
   Keywords : Machine Learning; Predicting House Prices; Shrinkage Methods; Random Forest; Decision Tree; AdaBoost; Gradient Boosting; LightGBM; CatBoost; XGBoost; Maskininlärning; Förutseende av Huspriser; Krympningsmetoder; Random Forest; Beslutsträd; AdaBoost; Gradient Boosting; LightGBM; CatBoost; XGBoost;

   Abstract : This thesis intends to evaluate the feasibility of supervised learning models for predicting house prices on the countryside of South Sweden. It is essential for mortgage lenders to have accurate housing valuation algorithms and the current model offered by Booli is not accurate enough when evaluating residence prices on the countryside. READ MORE

6. 5. Predicting house prices with machine learning methods

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Isak Engström; Alan Ihre; [2019]
   Keywords : ;

   Abstract : In this study, the machine learning algorithms k-Nearest-Neighbours regression (k-NN) and Random Forest (RF) regression were used to predict house prices from a set of features in the Ames housing data set. The algorithms were selected from an assessment of previous research and the intent was to compare their relative performance at this task. READ MORE