Log in

Relevant bibliographies by topics / House Price Prediction / Dissertations / Theses

To see the other types of publications on this topic, follow the link: House Price Prediction.

Dissertations / Theses on the topic 'House Price Prediction'

Author: Grafiati

Published: 4 June 2021

Last updated: 27 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 15 dissertations / theses for your research on the topic 'House Price Prediction.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.

1

Aghi, Nawar, and Ahmad Abdulal. "House Price Prediction." Thesis, Högskolan Kristianstad, Fakulteten för naturvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-20945.

Full text

Abstract:

This study proposes a performance comparison between machine learning regression algorithms and Artificial Neural Network (ANN). The regression algorithms used in this study are Multiple linear, Least Absolute Selection Operator (Lasso), Ridge, Random Forest. Moreover, this study attempts to analyse the correlation between variables to determine the most important factors that affect house prices in Malmö, Sweden. There are two datasets used in this study which called public and local. They contain house prices from Ames, Iowa, United States and Malmö, Sweden, respectively.The accuracy of the

APA, Harvard, Vancouver, ISO, and other styles

2

Engström, Isak, and Alan Ihre. "Predicting house prices with machine learning methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260140.

Full text

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. Software implementations for the experiment were selected from the scikit-learn Python library and executed to calculate the error between the actual and predicted sales price using four different metrics. Hyperparameters for the algorithms used were op

APA, Harvard, Vancouver, ISO, and other styles

3

Sollander, Robin. "Prediktion av huspriser i Falun / Prediction of House Prices in Falun." Thesis, KTH, Matematik (Avd.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-105808.

Full text

Abstract:

I denna uppsats tillämpas multipel regressionsanalys med syfte att predikera huspriser i Falun. Data som består av dels priset vid ett antal husförsäljningar och dels ett antal eventuellt samvarierande förklarande variabler analyseras. Två lämpliga, modeller som på ett så precist och enkelt sätt som möjligt förutsäger en kommande försäljning av ett hus tas fram. I den första finns en mäklarfirmas utropspris med som förklarande variabel i den andra inte. Prediktionsförmågan för de båda modellerna blir inte användbar i praktiken men bättre då utropspris finns med. De förklarande variablerna blir

APA, Harvard, Vancouver, ISO, and other styles

4

Oxenstierna, Johan. "Predicting house prices using Ensemble Learning with Cluster Aggregations." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-345157.

Full text

Abstract:

The purpose of this investigation, as prescribed by Valueguard AB, was to evaluate the utility of Machine Learning (ML) models to estimate prices on samples of their housing dataset. Specifically,the aim was to minimize the Median Absolute Percent Error (MDAPE) of the predictions. Valueguard were particularly interested in models where the dataset is clustered by coordinates and/or attributes in various ways to see if this can improve results. Ensemble Learning models with cluster aggregations were built and compared against similar model counterparts which do not partition the data. The weak

APA, Harvard, Vancouver, ISO, and other styles

5

Revend, War. "Predicting House Prices on the Countryside using Boosted Decision Trees." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279849.

Full text

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. Different types of boosted decision trees were implemented to address this issue and their performances were compared to traditional machine learning methods. These different types of supervised learning models were implemented in order to find the best model

APA, Harvard, Vancouver, ISO, and other styles

6

LIN, YI-PING, and 林翊平. "Data Mining in House Price Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/5sqy36.

Full text

Abstract:

碩士<br>銘傳大學<br>資訊工程學系碩士班<br>107<br>Real estate refers to buildings (civil law) that are immovable and can be fixed at the address, including the land and the houses on it. For the public, the house or the home is included in the food and clothing industry - live, this focus. Therefore, buying a house is a big problem that almost everyone must face. Whether it is seeking to buy or sell between real estate agents or friends or relatives, it is inevitable that the price will be mentioned in the end, that is, the price of house prices depends on the factors of the house itself. The surrounding envi

APA, Harvard, Vancouver, ISO, and other styles

7

Lai, Chih-Pin, and 賴智彬. "Prediction of House Price Index in Taipei." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/6w5p3s.

Full text

Abstract:

碩士<br>國立高雄應用科技大學<br>金融資訊研究所<br>102<br>Domestic prices have been high in recent years, April 2013, Mr. Minister of Finance Mr. Zhang Shenghu Taipei Vice Mayor Zhang Jin and held a press conference and hoped to lower 30% of real estate price by tax. It refers that the real estate market is very hot now. Discard of government intervention, the purpose of the study is whether we can use the price index correlation with other macroeconomic variables to predict future price movements and the factors to affect the fluctuation of real estate market in Taiwan, in order to provide reference for the acad

APA, Harvard, Vancouver, ISO, and other styles

8

Po-HungChen and 陳柏宏. "Integrated covariate correlative andgeographically weighted model for house price prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7q62hf.

Full text

Abstract:

碩士<br>國立成功大學<br>測量及空間資訊學系<br>107<br>House prices are affected by numerous reasons. Government policies, economic growth, and land use may cause regional housing price fluctuations. Thus, the housing price estimation problem will be challenging. This study aims to provide simple and efficient ways to estimate house prices. The normal regression model may not the best choice for estimation due to different house prices and heterogeneities in space. This paper proposed a covariate weighted regression (CWR) house price estimation model, which is an extended geographically weighted regression (GWR)

APA, Harvard, Vancouver, ISO, and other styles

9

Lin, Yu-Chuan, and 林右詮. "A House Price Prediction Integrated Web Service System of Taiwan Districts." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/5uh826.

Full text

Abstract:

碩士<br>國立交通大學<br>資訊科學與工程研究所<br>104<br>Buying a house for most people is not an easy thing in Taiwan. Therefore, they have to consider many factors. For houses’ appearance factors and for geographical factors, these factors will effect directly or indirectly effect the value of house itself. Before buying house, house buyers maybe use Internet to inquire the information of house for sale. And there are many websites of house for sale on the internet such as Yungching.com, Etwarm.com, Xinyi.com, etc. For house buyers, they always concern about the trend of house price of the house they want. Beca

APA, Harvard, Vancouver, ISO, and other styles

10

Hsieh, Zih-Chen, and 謝子宸. "House Price Prediction Model with Consideration of Neighborhood Features – A Case Study of Taiwan." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nm783x.

Full text

Abstract:

碩士<br>國立交通大學<br>網路工程研究所<br>106<br>Nowadays, most research and services of house price prediction in Taiwan focus on house characteristics and seldom take neighborhood/environmental features into consideration. However, attributes that affect house price not only contain house attributes, but also include neighborhood attributes and temporal trend of house price. This thesis used open data of real estate transaction data in Taipei City and New Taipei City, analyzed the effect of neighborhood attributes on house price to define a set of effective neighborhood attributes, and finally proposed a m

APA, Harvard, Vancouver, ISO, and other styles

11

Wang, Wenjing. "Daily House Price Indexes: Volatility Dynamics and Longer-Run Predictions." Diss., 2014. http://hdl.handle.net/10161/8694.

Full text

Abstract:

<p>This dissertation presents the construction procedure of “high-frequency” daily measure of changes in housing valuations, and analyzes its return dynamics, as well as investigates its relationship to capital markets. The dissertation consists of three chapters. The first chapter introduces the house price index methodologies and housing transaction data, and reviews the related literature. The second chapter shows the construction and modeling of daily house price indexes and highlights the informational advantage of the daily indexes. The final chapter provides detailed empiric

APA, Harvard, Vancouver, ISO, and other styles

12

Liang, Chih-Pin, and 梁志彬. "House Prices Prediction System Based on Open Government Data." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/03993199190401303124.

Full text

Abstract:

碩士<br>國立交通大學<br>網路工程研究所<br>103<br>It is a great expense for most people to buy the house. Thus, it is necessary to assess the information related to house carefully, especially the cost. If we can predict the house prices before buying it, it would be the important information for home buyers. They can decide whether to buy the house and bargain house price according to the prediction result. With the popularity of open data, the situation of information asymmetry between buyers and sellers disappears gradually. The service via data analysis and data visualization emerges in recent years. Most

APA, Harvard, Vancouver, ISO, and other styles

13

Lin, Hui-Chen, and 林慧珍. "A Prediction Model of House Prices for Town House and Store House in the East District, Tainan." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/57332162504698740518.

Full text

Abstract:

碩士<br>長榮大學<br>土地管理與開發研究所<br>96<br>Most of researches regarding the house prices model in recent years are applied with Hedonic Price Method, Artificial Neural Network and Back-propagation Neural Network, and have obtained quite significant results. The time-dependent house prices are often discussed from the angle of structural transformation because they (including characteristic factors) often change accompanying the time and space. Besides, the prices of town house and store house vary largely, so the price prediction (evaluation) is difficult. This research utilizes the data of house price

APA, Harvard, Vancouver, ISO, and other styles

14

Lee, Pai-Cheng, and 李百晟. "The Research on Multiple Regression Predictive Models of Taipei City House Price by Using House Construction Characteristics and its Surrounding Facilities." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/4us76t.

Full text

Abstract:

碩士<br>輔仁大學<br>統計資訊學系應用統計碩士在職專班<br>104<br>Because many factors determine prices, the real estate practice is difficult to assess whether the price is reasonable. For most people, home purchase is the largest expenditure for a lifetime. Buy a house has become one of Taiwan people's sources of stress. As the study for the purpose of the existing house in Taipei, to integrate the open data, "Real estate transaction data," "Taipei landmark information" and "Taipei Statistics Database", then create the new data set. After classifying the twelve area into six groups with CART, do the Factor Analys

APA, Harvard, Vancouver, ISO, and other styles

15

HUANG, CHIAO-YIN, and 黃巧音. "A Study on the Model Combination for Predictive Performance－Takes the House Price of Taiwan as an Example." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/mexk47.

Full text

Abstract:

碩士<br>僑光科技大學<br>財務金融研究所<br>107<br>According to the Sinyi Realty house price index, the index rose by 187.2% to the fourth quarter of 2018 (equal to 287.2) based on the house price index in Taiwan in the first quarter of 2001 (equal to 100). In other words, the house price of Taiwan has risen for a long time. Recently, however, the house price has fallen step by step as a result of the Government's housing hoarding tax and the two-tax system. Therefore, for the investors , how to predict future house price, it has become an important issue. Based on this, this paper uses a random walk model and

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!