You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Many brokers have adapted their operation to exploit the potential of the web. Despite the importance of the real estate classifieds, there has been little work in analyzing such data. In this paper we propose a two-stage regression model that exploits the textual data in real estate classifieds. We show how our model can be used to predict the price of a real estate classified. We also show how our model can be used to highlight keywords that affect the price positively or negatively. To assess our contributions, we analyze four real world data sets, which we gathered from three different property websites. The analysis shows that our model (which exploits textual features) achieves significantly lower root mean squared error across the different data sets and against variety of regression models.

Cited Abstract(s):
[1]: The object of this paper is to introduce our development experience of a data mining system for prospective real estate sellers and buyers to determine properties price. The prediction of continuous values of properties selling prices is modeled by a statistical technique called predictive regression. The prerequisite of this data mining process is to design a data warehouse that contains a wide variety of real estate listings in the related areas. The data source is extracted from multiple listing services (MLS) database. It is cleansed and transformed at a staging area. The data warehouse design for this system is a star schema with one large fact table surrounded by three dimension tables. Loading data into the warehouse is the final step in creating data warehouse as preparation for data mining. Oracle data warehousing tool kits were used in the data warehouse construction. Visual Basic .Net was used to implement the data mining system.

[2]: This paper describes a first effort to design and implement an adaptive neuro-fuzzy inference system based approach to estimate prices for residential properties. The data set consists of historic sales of homes in a market in Midwest USA and it contains parameters describing typical residential property features and the actual sale price. The study explores the use of fuzzy inference systems to assess real estate property values and the use of neural networks in creating and fine tuning the fuzzy rules used in the fuzzy inference system. The results are compared with those obtained using a traditional multiple regression model. The paper also describes possible future research in this area.

[3]: This paper presents the insights gained from applying data mining techniques, in particular neural networks for the purposes of developing an intelligent model used to predict real estate property values based on variety of factors. A dataset of over one thousand transactions in real estate properties was used. The dataset included 15 variables obtained from the multiple listing system (MLS) database and captured information on transactions taking place during a period of three years. The results from applying data mining techniques to predict real estate values are promising. Future plans and recommendations for further expanding the study are given.
One of the early works [3] used decision tree and neural network techniques to predict the sale price of a house. The analysis used data with 15 numerical features that represent the houses’ characteristics plus a categorical feature that corresponds to the address. The dataset consisted of 1000 records that were collected from the houses’ sales transactions in Miami, US. Unlike our work, the analysis focused only on properties for sale (did not include rentals), used only structured features (no text mining) and relied on a much smaller dataset (compared to our +50,000 records). A broader analysis was conducted in [1] , covering 295,787 transactions from four cities in the US. Again, only numerical features were used (although more extensive features were used, almost 200) and no textual features were used (also despite attempting to predict the price, no performance criterion was reported). A more recent work [2] proposed Adaptive Neuro Fuzzy Inference System (ANFIS) and tested the system over 360 records of past sales properties in Midwest, US. The dataset had 14 numerical features and again no textual feature was used.