High Dimensional Data Clustering using Self-Organized Map

As the population grows and e economic development, houses could be one of basic needs of every family. Therefore, housing investment has promising value in the future. This research implements the Self-Organized Map (SOM) algorithm to cluster house data for providing several house groups based on the various features. K-means is used as the baseline of the proposed approach. SOM has higher silhouette coefficient (0.4367) compared to its comparison (0.236). Thus, this method outperforms k-means in terms of visualizing high-dimensional data cluster. It is also better in the cluster formation and regulating the data distribution

The role of neural network for estimating real estate prices value in post COVID-19: a case of the middle east market

The main goal of this paper was to explore the use of an artificial neural network (ANN) model in predicting real estate prices in the Middle East market. Although conventional modeling approaches such as regression can be used in prediction, they have a weakness of a predetermined relationship between input and output. In this regard, using the ANN model was expected to reduce the bias and ensure non-linear relationships are also covered in the prediction process for more accurate results. The ANN model was created using Python v.3.10 program. The model exhibited a high correlation between predicted and actual house price data (R=0.658). In this respect, it was realized that the model could be effectively used in appraising real estate by investors. However, a major limitation of the model was realized to be a limited dataset for large and luxurious houses, which were not accurately predicted as data distribution between actual and predicted values became sparse for high house prices. A key recommendation made is that future research should include more variables related to luxurious houses and macroeconomic factors to increase the ANN model accuracy

A systematic review on spatial-based valuation approach for built cultural heritage

This paper reviews previous research that has been carried out to assess the value of built cultural heritage based on spatial-based valuation approach. Built cultural heritage is classified as a special property and can be categorised under thin market due to limited transaction or being traded inactively in certain areas. It will age with time, which needs special attention by the local communities and authorities to sustain its cultural, historical and architectural values to be transmitted to future generations. A systematic review has been conducted to examine spatial characteristics that may affect the values of built cultural heritage, the spatial-based valuation approach and the impact of heritage properties on surrounding house prices located within specific radius or distance from the heritage properties. The finding shows that theoretical and empirical studies by the previous research have given some attention to address the concern regarding an effective method for assessing the values of built cultural heritage. It also suggests that there is lack of study on the spatial-based valuation approach for built cultural heritage and Spatial Hedonic Modelling (SHM) offers many opportunities for further investigation

House Price Classification using Clustering Algorithms

The housing segment is one of the most lucrative industries in almost all parts of the world, and with an emerging place like Dubai with global attraction the real-estate market is set to expand more. With this, there is an importance to be able to understand such markets with in-depth expertise which not only helps to be a subject matter expert, but also provide recommendations and insights to customers and stakeholders. According to Asteco, UAE would witness an addition of 38,500 apartments and 3,800 villas and Dubai is estimated to account the most with 30,000 flats and 3,500 villas in 2022. Abu Dhabi is expected to see around 2,000 residential units to be given to Reem Island, 2,000 each in Al Raha Beach and Yas Island and 1,200 in Saadiyat Island. In January 2022, more than 53% transactions were for ready properties and 47% for off-plan properties.(Frank, 2022) There are different methods to segment properties, which is mainly dependent on collecting information about the apartment done through real estate agents or construction groups, who provide the information publicly or on request. With the evolution of the housing market, evidently due to the expansion of population and other development scope in different regions, the need to develop effective marketing strategies with high quality content as well as relevance has become key to sustenance. It becomes a starting point to building relationships with consumers, and this can be done by marketing the appropriate property to the right customer groups through online email marketing, brochures as well as pamphlets. In this report, we plan to use commonly available datasets, which are basically Dubai property records collected. We plan to implement an unsupervised clustering technique on the data to segment apartments/properties based on different traits. Even before that, we would be going through the details of the dataset through some exploratory data analyses, and then cleaning the data for inconsistency and then finally clustering the apartment ids based on different traits

SWGARCH : an enhanced GARCH model for time series forecasting

Generalized Autoregressive Conditional Heteroskedasticity (GARCH) is one of most popular models for time series forecasting. The GARCH model uses the long run variance as one of the weights. Historical data is used to calculate the long run variance because it is assumed that the variance of a long period is similar to the variance of a short period. However, this does not reflect the influence of the daily variance. Thus, the long run variance needs to be enhanced to reflect the influence of each day. This study proposed the Sliding Window GARCH (SWGARCH) model to improve the calculation of the variance in the GARCH model. SWGARCH consists of four (4) main steps. The first step is to estimate the model parameters and the second step is to compute the window variance based on the sliding window technique. The third step is to compute the period return and the final step is to embed the recent variance computed from historical data in the proposed model. The performance of SWGARCH is evaluated on seven (7) time series datasets of different domains and compared with four (4) time series models in terms of mean square error and mean absolute percentage error. Performance of SWGARCH is better than the GARCH, EGARCH, GJR, and ARIMA-GARCH for four (4) datasets in terms of mean squared error and for five (5) datasets in terms of maximum absolute percentage error. The window size estimation has improved the calculation of the long run variance. Findings confirm that SWGARCH can be used for time series forecasting in different domains

Optimasi metode random forest menggunakan Principal Component Analysis untuk memprediksi harga rumah

ABSTRAK Investasi menjadi hal yang menarik, khususnya investasi di bidang properti. Pihak developer juga harus berhati hati dalam menentukan harga properti. Perlu diketahui setiap tahunnya baik jangka pendek ataupun jangka panjang harga properti semakin naik dan bahkan hampir tidak pernah turun. Dalam menentukan harga sering juga berdasarkan dengan fitur yang dimiliki rumah seperti konsep, lokasi, kamar tidur, dll. Untuk memprediksi harga rumah berdasarkan fiturnya random forest mempunyai performa yang bagus untuk prediksi harga rumah. Namun metode random forest memiliki kelemahan jika penggunaan variabel terlalu banyak maka proses pelatihan menjadi lebih lama serta pemilihan fitur yang cenderung memilih fitur yang tidak informatif. Salah satu cara yang digunakan untuk mengurangi fitur tanpa harus menghapus fitur yang lain yaitu menggunakan Principal Component Analysis. Dalam penelitian ini motede yang digunakan adalah Principal Component Analysis dan random forest. Hasil pelatihan model dapat disimpulkan bahwa penggunaan hasil evaluasi model yang menggunakan PCA memiliki tingkat error yang lebih kecil dan nilainya lebih konsisten yaitu dengan rata-rata 0.0253. Sedangkan hasil evaluasi tanpa PCA dan hanya menggunakan random forest memiliki nilai eror yang lebih besar yaitu dengan rata rata 0.03275. Waktu pelatihan dengan menggunakan model PCA memiliki waktu yang lebih cepat dengan rata-rata 5007 milidetik, sedangkan yang hanya menggunakan random forest tanpa PCA memiliki waktu rata-rata 6099 milidetik. مستخلص البحث الاستثمار شيء مثير للاهتمام ، وخاصة الاستثمار في العقارات. يجب على المطور أيضًا توخي الحذر في تحديد سعر العقار. وتجدر الإشارة إلى أنه في كل عام ، على المدى القصير والطويل ، تتزايد أسعار العقارات ولا تنخفض أبدًا. عند تحديد السعر ، غالبًا ما يعتمد أيضًا على ميزات المنزل مثل المفهوم والموقع وغرف النوم وما إلى ذلك. إن التنبؤ بأسعار المنازل بناءً على خصائصها (Random Forest) له أداء جيد في التنبؤ بأسعار المنازل. ومع ذلك ، فإن الطريقة (Random Forest) لها عيب أنه إذا كنت تستخدم الكثير من المتغيرات ، فستستغرق عملية التدريب وقتًا أطول ويميل اختيار الميزة إلى اختيار الميزات غير المفيدة. إحدى الطرق المستخدمة لتقليل الميزات دون الحاجة إلى إزالة الميزات الأخرى هي استخدام (Principal Component Analysis). الطريقة المستخدمة في هذا البحث هي (Principal Component Analysis) و (Random Forest). من نتائج تدريب النموذج ، يمكن استنتاج أن استخدام نتائج تقييم النموذج باستخدام (PCA) له معدل خطأ أقل وقيم أكثر اتساقًا ، بمتوسط 0.0253. في حين أن نتائج التقييم بدون (PCA) وباستخدام (Random Forest) فقط لها قيمة خطأ أعلى بمتوسط 0.03275. يتمتع وقت التدريب باستخدام نموذج PCA بوقت أسرع بمتوسط 5007 مللي ثانية ، في حين أن الوقت الذي يستخدم فقط (Random Forest) بدون (PCA) يبلغ متوسط وقت 6099 مللي ثانية. ABSTRACT Investment is an interesting thing, especially property investment. The developer must also be careful in determining the price of the property. It should be noted that every year, both short-term and long-term, property prices increase and rarely go down. In determining the price, it is often also based on the features of the house such as the concept, location, bedrooms, etc. To predict house prices based on their features, the random forest has a good performance for predicting house prices. However, the random forest method has the disadvantage that if you use too many variables, the training process will take longer and feature selection tends to select features that are not informative. One way to reduce features without removing other features is to use Principal Component Analysis. In this research, the method used is Principal Component Analysis (PCA) and Random Forest. From the results of model training, it can be concluded that the use of model evaluation results using PCA has a smaller error rate and more consistent values, with an average of 0.0253. While the results of the evaluation without PCA and using only Random Forest have a higher error value with an average of 0.03275. The training time using the PCA model has a faster time, with an average of 5007 milliseconds, while those using only random forest without PCA have an average time of 6099 milliseconds