PREDICTING HOUSING SALES IN TURKEY USING ARIMA, LSTM AND HYBRID MODELS

Ayşe SOY TEMÜR · Melek Akgün · Günay Temür

111 citations0/0 files storedJournal of Business Economics and Management

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Forecasting Techniques and Applications Housing Market and Economics Stock Market Forecasting Methods

Abstract

Having forecast of real estate sales done correctly is very important for balancing supply and demand in the housing market. However, it is very difficult for housing companies or real estate professionals to determine how many houses they will sell next year. Although this does not mean that a prediction plan cannot be created, the studies conducted both in Turkey and different countries about the housing sector are focused more on estimating housing prices. Especially the developing technological advances allow making estimations in many areas. That is why the purpose of this study is both to provide guiding information to the companies in the sector and to contribute to the literature. In this study, a 124-month data set belonging to the 2008 (1) - 2018 (4) period has been taken into account for total housing sales in Turkey. In order to estimate the time series of sales, ARIMA (Auto Regressive Integrated Moving Average as linear model), LSTM (Long Short-Term Memory as nonlinear model) has been used. As to increase the estimation, a HYBRID (LSTM and ARIMA) model created has been used in the application. When MAPE (Mean Absolute Percentage Error) and MSE (Mean Squared Error) values obtained from each of these methods were compared, the best performance with the lowest error rate proved to be the HYBRID model, and the fact that all the application models have very close results shows the success of predictability. This is an indication that our study will contribute significantly to the literature.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 111 citations exist upstream.

References

2003Time series forecasting using a hybrid ARIMA and neural network model 2018Time series forecasting of petroleum production using deep LSTM recurrent networks 2006ARIMA forecasting of primary energy demand by fuel in Turkey 1991The Allocation of Capital and Time over the Business Cycle 2014A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data 1983ARIMA model building and the time series analysis approach to forecasting 2007A new hybrid artificial neural networks and fuzzy regression model for time series forecasting 2018Forecasting Economics and Financial Time Series: ARIMA vs. LSTM

Cited by

2021Deep Learning Model for House Price Prediction Using Heterogeneous Data Analysis Along With Joint Self-Attention Mechanism 2023Modeling and forecasting CO2 emissions in China and its regions using a novel ARIMA-LSTM model 2021Prediction of gold price with ARIMA and SVM 2023Hybrid Forecasting Methods—A Systematic Review 2023An optimized hybrid methodology for short‐term traffic forecasting in telecommunication networks

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.