AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Big Data Mining and Analytics Article
PDF (52.2 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Text-Based Price Recommendation System for Online Rental Houses

Department of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China.
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30318, USA.
Show Author Information

Abstract

Online short-term rental platforms, such as Airbnb, have been becoming popular, and a better pricing strategy is imperative for hosts of new listings. In this paper, we analyzed the relationship between the description of each listing and its price, and proposed a text-based price recommendation system called TAPE to recommend a reasonable price for newly added listings. We used deep learning techniques (e.g., feedforward network, long short-term memory, and mean shift) to design and implement TAPE. Using two chronologically extracted datasets of the same four cities, we revealed important factors (e.g., indoor equipment and high-density area) that positively or negatively affect each property’s price, and evaluated our preliminary and enhanced models. Our models achieved a Root-Mean-Square Error (RMSE) of 33.73 in Boston, 20.50 in London, 34.68 in Los Angeles, and 26.31 in New York City, which are comparable to an existing model that uses more features.

Keywords

price recommendationnatural language processingsentence embeddingLong Short-Term Memory (LSTM)mean shift

References

[1]
K. Barron, E. Kung, and D. Proserpio, The sharing economy and housing affordability: Evidence from Airbnb, https://ssrn.com/abstract=3006832, 2018.
Crossref
[2]
S. Sheppard and A. Udell, Do Airbnb properties affect house prices? http://web.williams.edu/Economics/wp/SheppardUdellAir//bnbAffectHousePrices.pdf, 2016.
[3]
G. Quattrone, D. Proserpio, D. Quercia, L. Capra, and M. Musolesi, Who benefits from the sharing economy of Airbnb? in Proc. of the 25th International Conference on World Wide Web, Montreal, Canada, 2016, pp. 1385-1394.
Crossref
[4]
Y. Chen and K. Xie, Consumer valuation of Airbnb listings: A hedonic pricing approach, International Journal of Contemporary Hospitality Management, vol. 29, no. 9, pp. 2405-2424, 2017.
Crossref Google Scholar
[5]
C. Gibbs, D. Guttentag, U. Gretzel, L. Yao, and J. Morton, Use of dynamic pricing strategies by Airbnb hosts, International Journal of Contemporary Hospitality Management, vol. 30, no. 1, pp. 2-20, 2018.
Crossref Google Scholar
[6]
G. Zervas, D. Proserpio, and J. W. Byers, The rise of the sharing economy: Estimating the impact of Airbnb on the hotel industry, Journal of Marketing Research, vol. 54, no. 5, pp. 687-705, 2017.
Crossref Google Scholar
[7]
E. Tang and K. Sangani, Neighborhood and price prediction for San Francisco Airbnb listings, http://cs229.stanford.edu/proj2015/236_report.pdf, 2018.
[8]
Y. Li, Q. Pan, T. Yang, and L. Guo, Reasonable price recommendation on Airbnb using Multi-Scale clustering, in Proc. of the 35th Chinese Control Conference (CCC), Chengdu, China, 2016, pp. 7038-7041.
Crossref
[9]
Y. Li, S. Wang, T. Yang, Q. Pan, and J. Tang, Price recommendation on vacation rental websites, in Proc. of the 2017 SIAM International Conference on Data Mining, Galleria Houston, TX, USA, 2017, pp. 399-407.
Crossref
[10]
D. Wang and J. L. Nicolau, Price determinants of sharing economy based accommodation rental: A study of listings from 33 cities on Airbnb. com, International Journal of Hospitality Management, vol. 62, no. 1, pp. 120-131, 2017.
Crossref Google Scholar
[11]
S. Hochreiter and J. Schmidhuber, Long short-term memory, Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
Crossref Google Scholar
[12]
U. Kamath, J. Liu, and J. Whitaker, Recurrent neural networks, in Deep Learning for NLP and Speech Recognition. New York, NY, USA: Springer, 2019, pp. 315-368.
Crossref
[13]
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, Attention is all you need, in Proc. of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 5998-6008.
[14]
C. Z. A. Huang, A. Vaswani, J. Uszkoreit, N. Shazeer, C. Hawthorne, A. M. Dai, M. D. Hoffman, and D. Eck, An improved relative self-attention mechanism for transformer with application to music generation, arXiv preprint arXiv: 1809.04281, 2018.
[15]
S. Arora, Y. Liang, and T. Ma, A simple but tough-to-beat baseline for sentence embeddings, presented at International Conference on Learning Representations, Palais des Congrès Neptune, Toulon, France, 2017.
[16]
K. Fukunaga and L. Hostetler, The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on Information Theory, vol. 21, no. 1, pp. 32-40, 1975.
Crossref Google Scholar
[17]
K. G. Derpanis, Mean shift clustering, Lecture Notes, http://www.cse.yorku.ca/%7Ekosta/CompVis_Notes/mean_shift.pdf, 2005.
[18]
G. K. Tso and K. K. Yau, Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks, Energy, vol. 32, no. 9, pp. 1761-1768, 2007.
Crossref Google Scholar
[19]
D. Comaniciu and P. Meer, Mean shift: A robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002.
Crossref Google Scholar
[20]
L. Cao, J. Luo, A. Gallagher, X. Jin, J. Han, and T. S. Huang, A worldwide tourism recommendation system based on geotagged web photos, in Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, USA, 2010, pp. 2274-2277.
Crossref
[21]
Airbird Inside Airbnb Data, http://insideairbnb.com/get-the-data.htm, 2019.
[22]
J. Pennington, R. Socher, and C. Manning, Glove: Global vectors for word representation, in Proc. of the Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014, pp. 1532-1543.
Crossref
[23]
J. Gallin, The longrun relationship between house prices and rents, Real Estate Economics, vol. 36, no. 4, pp. 635-658, 2008.
Crossref Google Scholar
[24]
D. Lee, W. Hyun, J. Ryu,, W. J. Lee, W. Rhee, and B. Suh, An analysis of social features associated with room sales of Airbnb, in Proc. of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing, New York, NY, USA, 2015, pp. 219-222.
Crossref
[25]
P. Choudhary, A. Jain, and R. Baijal, Unravelling Airbnb predicting price for new listing, arXiv preprint arXiv: 1805.12101, 2018.
Big Data Mining and Analytics
Volume 3 Issue 2,
June 2020
Pages 143-152
DOI: 10.26599/BDMA.2019.9020023
Cite this article:
Shen L, Liu Q, Chen G, et al. Text-Based Price Recommendation System for Online Rental Houses. Big Data Mining and Analytics, 2020, 3(2): 143-152. https://doi.org/10.26599/BDMA.2019.9020023

1187

Views

107

Downloads

11

Crossref

11

Web of Science

11

Scopus

0

CSCD

Altmetrics
Received: 09 December 2019
Accepted: 10 December 2019
Published: 27 February 2020
© The author(s) 2020

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return