Real Estate

Real Estate Valuation


Real estate valuation is at the core of buying and selling properties. Getting the right price to sell a good is paramount to successfully close a deal. On the contrary, getting that initial offer number wrong can lead to either drastically undersell one’s property or simply not being able to find an acquirer.

Problems to solve

  • How can we predict the value of a house or an apartment? Which method can be used for a quick and objective appraisal?
  • Can machine learning help in these matters and how accurate can predictive models be to predict real prices?

Benefits of TADA

Real estate is the largest asset class in the world. It makes up, on average, 5.1% of any institutional portfolio (Andonov, Eichholtz, and Kok [2013]).

Finding the true market value of a property is an essential skill for appraisers, and it ensures a fair negotiation. Real estate professionals and investors can use predictive models to get realistic market values.

However, they are not data scientists and may not have the skills in machine learning nor the coding experience to build models. Moreover, they mostly handle Small Data, where historical data contain few hundreds or thousands of properties, but rarely millions (aka Big Data) in the same area. The machine learning tools that work well with Big Data may not perform as well with Small Data. 
By using an automated machine learning solution such as TADA, real estate professionals can now evaluate more quickly and accurately the price of their goods. Machine Learning holds great promise for real estate.

MyDataModels allows real estate professionals to build predictive models from Small Data automatically and without training. They can use their collected data directly, without normalization and outlier’s management nor feature engineering. Thanks to this limited data preparation, the results from this specific dataset were obtained with a few clicks in less than a minute on a regular laptop. 
The results obtained from MyDataModels’ predictive model are satisfying: in average, we make a mistake of up to $3,600 in our predictions.


Pricing is key in real estate, as people are willing to get the most accurate price for the property they wish to purchase or sell. Hence, it is vital for real estate agencies to provide them with a precise estimation of the property price.

To successfully determine property prices, real estate specialists need to extract value from all the available information to complement their domain expertise and help them close deals with their customers.

In this real estate valuation use case, the results obtained from MyDataModels’ predictive models are very satisfying with an average error of 16%.

By using an automated machine learning solution such as TADA, real estate specialists can now easily estimate the value of a property according to different environmental factors and data. This prediction is made quickly, with great precision, which allows them to move forward fast and provide their customers with the most accurate valuation for the property they wish to purchase or sell.

Case study


Automated Machine Learning solutions consist of predicting the future with historical data. To predict a future result, you must bring your descriptive data and the past result obtained.

TADA allows you to simply create a relevant predictive model from your data and apply it to future data.

In this case, the descriptive data is houses’ information.

The goal of the dataset is to predict the price of a house: it’s a regression task, meaning that the purpose of the model is to predict a numerical value.

To generate a model, the steps are the following:

  • Create your project and load your data as a CSV table (with data in rows and variables in columns).
  • Select the variable you want to predict, called Goal. 
    In this case, the Goal is the variable "Y_house_of_price_unit_area" (a visualization of the variable is provided).
  • Select your data for the model generation. This step is called "Creating the Variable set" and allows you to manually select the descriptive variables you want to use. By default, they are all selected.
    TADA identifies the relevant descriptive variables by itself, which affects the calculation time required to create the model.
    The fewer variables selected, the faster the model creation.
  • Create your model.
    At creation, default values ​​are proposed to you: Name of models, Population, Iteration. You only need to validate the default values ​​to start model generation.
    ‘Best practices’ are at your disposal to guide you in the choice of these parameters.

    Depending on the size of the descriptive data file, this step can take between a few seconds and ten minutes.
    Once the model is created, you can see the results of the model using metrics and charts so you can judge its relevance.


To apply a model that you think is relevant, you can:

  • Retrieve the associated mathematical formula and apply it (for instance on Excel)
  • Retrieve the source code of the formula and use it by yourself (Valid only on TADA paying offers). The source code is available in R, Java, C ++ and soon Python.
  • In order to use our "Predict" feature on the product, you will have to upload your file containing the data to be predicted. You will be returned a downloadable file containing the given data, with
    the calculated predictions.

Dataset information

Each row is a house and each column is a variable which can be used in the model. 

Historical values are shown in the last column of the table (“Y_house_price of unit area”).


Task Type: Regression
Number of variables: 7
Number of rows: 290 
Goal: Y= house price of unit area

Assessing the market value of real estate is a daunting task. The real estate market is exposed to many fluctuations in prices because of existing correlations with many variables, some of which cannot be controlled or might even be unknown. Housing prices can increase rapidly (or, in some cases, drop very fast).
Appraisers still manually evaluate the value of assets that are sometimes worth billions of dollars by comparing an asset to a small set of previously transacted reference buildings that are somehow comparable.
Machine Learning holds great promise for real estate pricing models.

This case study is based on real data from a public dataset originally found in the the UCI data repository (
The objective is the evaluation of the price of a house per square meter in Taiwan dollars (In the original dataset the surface unit used is the ‘Ping’, corresponding to 3.3 square meter. However, we kept the original unit). 
The figure below shows an extract of this public dataset. 

The model uses the variables as follows:

X1=the transaction date (for instance, 2013.250=2013 March, 2013.500=2013 June, etc.) 
X2=the house age (unit: year) 
X3=the distance to the nearest MRT station (unit: meter) 
X4=the number of convenience stores in the living circle on foot (integer) 
X5=the geographic coordinate, latitude (unit: degree) 
X6=the geographic coordinate, longitude (unit: degree) 

The Goal
Y= house price of unit area (square meter).  


The results of the model are available following the generation of the model.

They present the performance of the predictive model.

The type of predictive model and the measurement indicators of the associated model are related to the Goal (Variable to be predicted) and the values ​​of this variable.

The type of model you make is shown on the model results display.

According to the type of the Goal (in our case, the Goal is "Y_Price_of_unit_area"), we can make three types of predictions:

  • Binary classification: Discrete value taking only two values (yes / no for instance)
  • Multiclass classification: Discrete value taking more than two values (for instance a status of state with values ​​like: On, Risk of breakdown, Down, etc.)
  • Regression: Continuous value that can take an infinite number of values (a temperature, a pressure, a turnover, the price of a house, etc.)

At the generation of the model, and according to the practices and state of the art of Machine Learning, your dataset will be divided into three parts by TADA:

  • A training part which represents 40% of your dataset, it allows to train a certain number of formulas,
  • A validation part, which represents 30% of your dataset, which validates and selects the best formulas found in the previous step,
  • A test part which represents the last 30% of the model and which corresponds to the test of the formulas approved by the preceding stage. The performance measurement and the evaluation of your model should mainly be done on this partition (Standard and state of the art of Machine Learning) because the present data were not used in the learning and validation phase of the machine learning model and serve just to measure its performance.

How good is this model?

The metrics yielded by TADA under the Metrics heading are shown in the table below and refer to a run of one minute. 


We can make a few observations.

  • The Maximum error - defined as the difference between the actual value and the predicted one - can be negative.
  • Now, for every regression task, e.g. where a numeric value is predicted or ‘fitted’- we may judge the error of the model with respect to the standard deviation 𝜎 (the ‘spread’) of the data used to construct the model. A model displaying a prediction error of the same order of magnitude, indicates a good prediction. 
  • For our starting data, the spread of the price per unit surface is 𝜎= $4,260. This is a considerable spread around the mean value of $11,500 /sqm (or the close median value of 11,650). On the other hand, the regression results are of the same order of magnitude (with an RMSE around 3,600 dollars per square meter in fact). Thus, the model can be judged as acceptable, within the limits of the initial data quality.

The last point means that the model produces a prediction which is not more uncertain than the original data. Thus, it can only be as good as the data used to generate it. Clearly, we cannot do better and, in fact, enhance the quality of the initial data - nobody can!

Ready to use TADA?

You don't have immediate data?

No problem, data are available to make your trial as relevant as possible!

Try it now!

Detailed informations


Artificial intelligence: Theories and techniques aiming to simulate intelligence (human, animal or other).

Binary Classification: It is the problem type when you are trying to predict one of two states, e.g. yes/no, true/ false, A/B, 0/1, red/green, etc. This kind of analysis requires that the Goal variable type is of type CLASS. Binary Classification analysis also requires that there be only 2 different values in the Goal column. Otherwise, it is not a binary problem (two choices and no more).

Convolutional Neural Network: This type of network is dedicated to object recognition. They are generally composed of several layers of convolutions + pooling followed by one or more FC layers. A convolutional layer can be seen as a filter. Thus, the first layer of a CNN make it possible to filter the corners, curves and segments and the following ones, more and more complex forms.

Data Mining: Field of data science aimed at extracting knowledge and / or information from a body of data.

Deep Learning: Deep Learning is a category of so-called "layered" machine learning algorithms. A deep learning algorithm is a neural network with a large number of layers. The main interest of these networks is their ability to learn models from raw data, thus reducing pre-processing (often important in the case of classical algorithms).

Fully Convolutional Networks: An FCN is a CNN with the last FC layers removed. This type of network is currently not used much but can be very useful if it is succeeded by an RNN network allowing integration of the time dimension in a visual recognition analysis.

GRU (Gated Recurrent Unit): A GRU network is a simplified LSTM invented recently (2014) and allowing better predictions and easier parameterization.

LSTM (Long Short-Term Memory): An LSTM is an RNN to which a system has been added to control access to memory cells. We speak of "Gated Activation Function". LSTMs perform better than conventional RNNs.

Machine learning : Subfield of Artificial Intelligence (AI), Machine Learning is the scientific study of algorithms and statistical models that provides systems the ability to learn and improve any specific tasks without explicit programming.

Multi Classification: Classification when there is more than two classes in the goal variable, e.g. A/B/C/D, red/orange/green, etc.

Multilayer perceptron: This is a classic neural network. Generally, all the neurons of a layer are connected to all the neurons of the next layer. We are talking about Fully Connected (FC) layers.

RCNN (Regional CNN): This type of network compensates for the shortcomings of a classic CNN and answers the question: what to do when an image contains several objects to recognize? An RCNN makes it possible to extract several labels (each associated with a bounding box) of an image.

Regression: Set of statistical processes to predict a specific number or value. Regression analysis requires the type of Goal variable to be numeric (INTEGER or DOUBLE).

Reinforcement learning: Reinforcement learning is about supervised learning. It involves using new predicted data to improve the learning model (calculated upstream).

RNN (Recurrent Neural Networks): Recurrent networks are a set of networks integrating the temporal dimension. Thus, from one prediction to another, information is shared. These networks are mainly used for the recognition of activities or actions via video or other sensors.

Semi supervised learning: Semi-supervised learning is a special case of supervised learning. Semi-supervised learning is when training data is incomplete. The interest is to learn a model with little labeled data.

Stratified sampling: It is a method of sampling such that the distribution of goal observations in each stratum of the sample is the same as the distribution of goal observations in the population. TADA uses this method to shuffle the data set from binary and multi classification projects.

Simple random sampling: It is a method of sampling in which each observation is equally likely to be chosen randomly. TADA uses this method to shuffle the data set from regression projects.

Supervised learning: Sub-domain of machine learning, supervised learning aims to generalize and extract rules from labeled data. All this in order to make predictions (to predict the label associated with a data without label).

Transfer learning: Brought up to date by deep learning, transfer learning consists of reusing pre-learned learning models in order not to reinvent the wheel at each learning.

Unsupervised learning: Sub-domain of machine learning, unsupervised learning aims to group data that are similar and divide/separate different data. We talk about minimizing intra-class variance and maximizing inter-class variance.



ACC (Accuracy): Percentage of samples in the test set correctly classified by the model.

Actual Negative: Number of samples of negative case in the raw source data subset.

Actual Positive: Number of samples of positive case in the raw source data subset.

AUC: Area Under the Curve (AUC) of the Receiver Operating characteristic (ROC) curve. It is in the interval [0;1]. A perfect predictive model gives an AUC score of 1. A predictive model which makes random guesses has an AUC score of 0.5.

F1 score: Single value metric that gives an indication of a Binary Classification model's efficiency at predicting both True and False predictions. It is computed using the harmonic mean of PPV and TPR.

False Negative: Number of positive class samples in the source data subset that were incorrectly predicted as negative.

False Positive: Number of negative class samples in the source data subset that were incorrectly predicted as positive.

MCC (Matthews Correlation Coefficient): Single value metric that gives an indication of a Binary Classification model's efficacy at predicting both classes. This value ranges between -1 to +1 with +1 being a perfect classifier.

PPV (Positive Predictive Value/Precision): Number of a model's True Positive predictions divided by the number of (True Positives + False Positives) in the test set.

Predicted Positive: Number of samples in the source data subset predicted as the positive case by the model.

Predicted Negative: Number of samples in the source data subset predicted as the negative case by the model.

True Positive: Number of positive class samples in the source data subset accurately predicted by the model.

True Negative: Number of negative class samples in the source data subset accurately predicted by the model.

TPR (True Positive Rate/Sensitivity/Recall): Ratio of True Positive predictions to actual positives with respect to the test set. It is calculated by dividing the true positive count by the actual positive count.

TNR (True Negative Rate/Specificity): Ratio of True Negative predictions to actual negatives with respect to the test set. It is calculated by dividing the True Negative count by the actual negative count.


Multi classification

ACC (Accuracy): Ratio of the correctly classified samples over all the samples.

Actual Total: Total number of samples in the source data subset that were of the given class.

Cohen’s Kappa (K): Coefficient that measures inter-rater agreement for categorical items, it tells how much better a classifier is performing over the performance of a classifier that simply guesses at random according to the frequency of each class. It is in the interval [-1:1]. A coefficient of +1 represents a perfect prediction, 0 no better than random prediction and −1 indicates total disagreement.

False Negative: Number of positive class samples in the source data subset that were incorrectly predicted as negative.

False Positive: Number of negative class samples in the source data subset that were incorrectly predicted as positive.

Macro-PPV (Positive Predictive Value/Precision): The mean of the computed PPV within each class (independently of the other classes). Each PPV is the number of True Positive (TP) predictions divided by the total number of positive predictions (TP+FP, with FP for False Positive) within each class. PPV is in the interval [0;1]. The higher this value, the better the confidence that positive results are true.

Macro-TPR (True Positive Rate/Recall): The mean of the computed TPR within each class (independently of the other classes). Each TPR is the proportion of samples predicted Truly Positive (TP) out of all the samples that actually are positive (TP+FN, with FN for False Negative). TPR is in the interval [0;1]. The higher this value, the fewer actual samples of positive class are labeled as negative.

Macro F1 score: Harmonic mean of macro-average PPV and TPR. F1 Score is in the interval [0;1]. The F1 Score can be interpreted as a weighted average of the PPV and TPR values. It reaches its best value at 1 and worst value at 0.

MCC (Matthews Correlation Coefficient): Represents the multi class confusion matrix with a single value. Precision and recall for all the classes are computed and averaged into a single real number within the interval [-1;1]. However, in the multiclass case, its minimum value lies between -1 (total disagreement between prediction and truth) and 0 (no better than random) depending on the data distribution.

Predicted Total: Total number of samples in the source data subset that were predicted of the given class.

True Positive: Number of positive class samples in the source data subset accurately predicted by the model.

True Negative: Number of negative class samples in the source data subset accurately predicted by the model.



MAE (Mean Absolute Error): represents the average magnitude of the errors in a set of predictions, without considering their direction. It’s the average over the test sample of the absolute differences between prediction and actual observation where all individual differences have equal weight. MAE is in the intervall [0;+∞]. A coefficient of 0 represents a perfect prediction, the higher this value is the more error (relative error) the model have.

MAPE (Mean Absolute Percentage Error): MAPE is computed as the average of the absolute values of the deviations of the predicted versus actual values.

Max-Error: Maximum Error. The application considers here the magnitude (absolute error when identifying the maximum error. Thus -1.5 would be consider the maximum error over +1.3. The sign of the error however is still reported in this column in case it has domain significance for the user.

R2 (R Squared): also known as the Coefficient of Determination. The application computes the R2 statistic as 1 - (SSres / SStot) where SSres is the residual sum of squares and SStot is the total sum of squares.

RMSE: Root Mean Square Error against the Dataset partition selected. RMSE is computed as the square root of the mean of the squared deviations of the predicted from actual values.

SD-ERROR (Standard Deviation Error): Standard statistical measure used to quantify the amount of variation of a set of data values.