# House Price Prediction Based On Deep Learning

arXiv: https://arxiv.org/abs/2204.09050 · HTML: https://mlsanity.com/paper/2204.09050/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 56 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Impact of ImageNet Model Selection on Domain Adaptation](https://mlsanity.com/paper/2002.02559/) (2020)
- [Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition](https://mlsanity.com/paper/2004.10016/) (2020)
- [House price estimation from visual and textual features](https://mlsanity.com/paper/1609.08399/) (2016)
- [Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2105.02089/) (2021)
- [Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet](https://mlsanity.com/paper/1904.02322/) (2019)
- [Adversarial Consistent Learning on Partial Domain Adaptation of PlantCLEF 2020 Challenge](https://mlsanity.com/paper/2009.09289/) (2020)
- [What Image Features Boost Housing Market Predictions?](https://mlsanity.com/paper/2107.07148/) (2021)
- [Enhanced Separable Disentanglement for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.11915/) (2021)
- [A Regressive Convolution Neural network and Support Vector Regression Model for Electricity Consumption Forecasting](https://mlsanity.com/paper/1810.08878/) (2018)
- [Automated identification of hookahs (waterpipes) on Instagram: an application in feature extraction using Convolutional Neural Network and Support Vector Machine classification](https://mlsanity.com/paper/1810.08881/) (2018)
- [Adversarial Regression Learning for Bone Age Estimation](https://mlsanity.com/paper/2103.06149/) (2021)
- [Correlated Adversarial Joint Discrepancy Adaptation Network](https://mlsanity.com/paper/2105.08808/) (2021)
- [Electricity consumption forecasting method based on MPSO-BP neural network model](https://mlsanity.com/paper/1810.08886/) (2018)

## Abstract (arXiv)

Since ancient times, what Chinese people have been pursuing is very simple, which is nothing more than "to live and work happily, to eat and dress comfortable". Today, more than 40 years after the reform and opening, people have basically solved the problem of food and clothing, and the urgent problem is housing. Nowadays, due to the storm of long-term rental apartment intermediary platforms such as eggshell, increasing the sense of insecurity of renters, as well as the urbanization in recent years and the scramble for people in major cities, this will make the future real estate market competition more intense. In order to better grasp the real estate price, let consumers buy a house reasonably, and provide a reference for the government to formulate policies, this paper summarizes the existing methods of house price prediction and proposes a house price prediction method based on mixed depth vision and text features.