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Using Object Detection in Real Estate for Home Price Estimation

Object detection in real estate

Judging the price of a property accurately is one of the most crucial factors in real estate businesses. Buyers access several aspects before purchasing a property. The characteristics that influence a home sale price will generally include the square footage area, location, number of rooms, and the interiors of the house. Other factors that influence the appeal are streets around the house, safety of the area, and its access to commercial businesses, schools, and hospitals. 

Machine learning is now playing an important role in assessing real estate property prices. AI has changed the way businesses are done and real estate is no exception. Since machine learning has increased the efficiency of buying and selling properties. Estimating a home price becomes more convenient and accurate when machine learning is incorporated along with human judgment. 

Computer vision and object detection has great potential in the field of real estate. Since it can be utilized to diversify markets and improve user experience. Object detection can identify every detail within an image of a room and access its value for real estate pricing. Key features in the image such as a table, chair, window, cabinet, bouquet, sofa, bed, lamp, painting, curtain, etc. can be identified to deliver useful information about the room. 

For example object detection can help understand the levels of daylight in a room, whether it has a fireplace, or the kind of flooring in the room. Here, we explore the application of object detection in real estate for home price estimation.

Computer vision in real estate

Use cases that object detection solves in real estate

The real estate industry is already using object detection to identify and categorize different images. Let’s take a look at some of the use cases that object detection solves:

  • Real estate images are not always clear. Object detection helps accurately detect objects in low-light and out-of-focus images.
  • It can be used to find exact locations of objects in an image, detect the number of times the object appears, or measure its size using the dimensions of the bounding box.
  • Additional characteristics that can be identified through object detection include terms like ‘bright and spacious’, ‘new oven’, ‘wooden floor’, ‘high ceiling’, ‘manicured garden’, etc.
  • Object detection allows comparing the same room in different properties. For example, the living room in multiple homes can be compared with the help of this technique.

Skyl.ai real estate object detection machine learning model

Object detection systems are now capable of detecting objects from images. This is highly useful for the real estate industry as it can be used for detecting objects in residential and commercial properties to get the real price estimation. Property listings are usually updated manually, and the process is not able to cope with the amount of information that must be regularly updated. Therefore, the quality of information available may not be up-to-the-mark and properly represent the actual property.

Skyl.ai’s computer vision model allows automatic detection of objects in property images and updates information on real estate platforms. To build this machine learning model we have collected 230 images from a public data repository.

Data Labeling

Data Labeling -1

In this model, the bounding box is used as the data labeling method. Labeling can be done for different rooms like the living room, kitchen, dining room, etc. The image selected here is of the bedroom.

There are different categories of labels such as bed, lamp, picture, curtain, etc. The data overview shows the number of objects in the image. The platform provides data visualization tools to identify the distribution of various objects in the image.

Data set overview Data set overview

Feature set Engineering

In the next step, the feature sets or subsets of the data set are created for machine learning model training. The random split used for the feature set is 90:10. Aspects such as extracting data or letting the platform manipulate ratios of training and testing can be customized here. This step is important as the entire dataset cannot be used to create an accurate model.

Feature set engineering

Model Training

At this stage, the model is trained with a small feature set with equally distributed labeled categories. You can select the feature set you want to use to train the model and notes can be made on the model description. 

Skyl.ai uses Faster RCNN (Transfer Learning with Resnet-50) in this model. Faster R-CNN passes the entire image to a Convolutional Neural Network that generates the regions of interest. Features from these regions of interest are extracted and classified, finally returning the location of the area of interest.

Model Training 1

Model Training 2

Once the model training is complete, the system can be deployed. After deployment and implementation, it is found that the machine learning model can identify different objects present in the real estate image with great accuracy. The model locates/detects the area and then classifies the objects.

Skyl.ai’s computer vision model automates object detection in real estate images, detecting objects like cabinet, sofa, window, painting, lamp, bed, chair, curtain, sink, oven, plant, cupboard, fridge, stove, etc., for home price estimation. It’s important to detect these factors as they help match human expectations and social norms, therefore helping in accurate price estimation. The machine learning model uses state-of-the-art neural network technology to identify different objects in images. The input for this machine learning model is various images of home objects and the image data is labeled using Skyl Labelwise. 

Skyl.ai’s machine learning model helps take into consideration the objects and factors that may create value and improve the appeal of the property for a buyer.  The cutting-edge technology ensures assessing of the right cost for a property.

    

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