When the IRS Levies Estate Property, Whose Fight is it? – Houston Tax Attorneys


When a taxpayer dies with unresolved IRS issues—unpaid taxes, disputed levies, or unrefunded overpayments—the family often assumes that whoever inherits the estate can pick up where the decedent left off. That assumption might not be the correct.

The tax code gives specific rights to specific parties. When the wrong person shows up in federal court to assert those rights, the courthouse door can close before the merits ever get heard. This is more than a procedural technicality.

A personal representative of an estate has both the authority and, under state law, a fiduciary obligation to recover assets belonging to the estate—including money the IRS may have wrongfully taken. A beneficiary, no matter how large a share of the estate they stand to receive, does not step into that role automatically. The distinction matters when it comes to suing the IRS over improper tax collection.

The recent federal district court decision in Hafner v. United States, No. 2:25-cv-01170 (W.D. Wash Feb. 13, 2026) raises the question as to when the IRS has wrongfully levied estate property or failed to issue a refund, can the estate get that money back, and whose fight is it? Is it the beneficiary’s fight or the personal representative of the estate’s fight?

Facts & Procedural History

The taxpayer in this case is the beneficiary of the estate of the Decedent Hafner. The taxpayer was the estate’s former administrator or personal representative in the probate.

The decedent’s estate apparently overpaid taxes and was subject to IRS collection activity—including what the taxpayer alleged was a wrongful levy and unauthorized collection conduct—before the decedent’s death.

After the decedent died, the taxpayer filed suit in federal district court seeking several forms of relief. He sought recovery of alleged tax overpayments under Section 6402 of the tax code based on the claim that the refunds belong to him as the estate’s beneficiary. He also sought damages for the wrongful levy of estate property under Section 7426 and damages for unauthorized IRS collection conduct under Section 7433. There were other claims too.

The IRS did not dispute the claims per se. Instead, it moved to dismiss the case for lack of subject-matter jurisdiction under Rule 12(b)(1) of the Federal Rules of Civil Procedure. The IRS asserted that the government was immune from the suit and, separately, that the taxpayer lacked standing to bring any of the claims.

Who Has Standing to Sue the IRS?

The threshold question in every federal lawsuit is who has standing to sue the IRS? Does this plaintiff have standing to be in court at all?

Standing is a constitutional requirement grounded in Article III of the Constitution. It limits federal courts to deciding actual cases or controversies. If a plaintiff cannot demonstrate standing, the case ends there—regardless of how valid the underlying claim might be.

In the tax context, standing overlaps with the language of the specific statutes that authorize suits against the government. Because the government is generally immune from suit, it has what is known as “sovereign immunity”, Congress has ot affirmatively waive that immunity before a taxpayer can sue.

When Congress does so, the waiver tends to be narrow. Courts read it carefully. This is why the language of each tax code section that waives immunity matters so much. Each statute the taxpayer invoked in this case had its own specific requirement about who could bring the claim.

Tax Refunds Under § 6402

Section 6402 of the tax code is the general refund provision. It directs the IRS to refund any overpayment to “such person”—meaning the person who made the overpayment. The Supreme Court confirmed this reading by explaining that the phrase “the person who made the overpayment” simply identifies who is entitled to the refund.

Other courts have noted that Section 6402 requires refunds to be made to the person who actually made the overpayment.

This may sound straightforward, but it has real consequences in the estate context as we have here. When a decedent overpays taxes during their lifetime, or when the estate itself overpays, neither the decedent nor the estate is the same legal entity as the beneficiary. The beneficiary did not make the overpayment. The right to that IRS tax refund offset or recovery belongs to whoever actually paid—which is the decedent or the estate, not the person who stands to inherit.

In this case, the taxpayer did not allege that he personally made the overpayments. He conceded that the decedent and the estate made them. The court found that dispositive. Without having made the overpayment, the taxpayer had no standing to demand the refund under Section 6402.

Wrongful Levy Claims Under § 7426

A levy by the IRS is one of the government’s most powerful collection tools. It allows the IRS to seize wages, bank accounts, and other property to satisfy unpaid tax debts. When that power is misused, or when it sweeps up property belonging to someone other than the delinquent taxpayer, Section 7426 is the remedy. But the statute requires that the claimant have “an interest” in the property — and the courts have held that this interest must exist at the time of the levy.

When the IRS seizes property to satisfy a tax debt, and a third party—someone other than the taxpayer—has a legitimate interest in that property, Section 7426 allows that third party to file a civil action against the government. The statute specifically covers any person who “claims an interest in or lien on” the property that was wrongfully seized.

The Supreme Court have said that the courts are look to state property law to determine the nature of the legal interest the claimant held. So state law controls the question of what kind of interest qualifies.

In this case, the government argued that the levy occurred while the decedent was still alive. This meant that the taxpayer—as a mere potential beneficiary at that point—had no cognizable property interest under Washington law. The court had previously dismissed a similar claim after finding that the plaintiff lacked “an interest” in the levied property under Washington law at the time of the levy.

The court noted that the taxpayer offered no Washington l…

The court noted that the taxpayer offered no Washington law authority showing that a beneficiary holds a protectable interest in estate property while the decedent is still living. Without that, the taxpayer could not meet the threshold requirement of Section 7426, and the wrongful levy claim failed for lack of standing.

Unauthorized Collection Damages Under § 7433

When IRS officers or employees disregard provisions of the tax code or its regulations in the course of collecting a tax, Section 7433 allows “such taxpayer” to bring a civil action for damages.

The phrase “such taxpayer” does real work here. Courts have consistently held that it means the person from whom the IRS was actually collecting—not a third party, and not a family member.

The courts have also considered cases involving this rule. The cases generally explaine that standing under Section 7433 requires the plaintiff to be “such taxpayer”—the direct taxpayer from whom the IRS collected the tax, not a third party.

In this case, the decedent and the estate were the taxpayers. The beneficiary was not. The IRS’s collection conduct—however improper it may have been—was directed at the decedent and the estate, not at the taxpayer bringing the lawsuit. The Section 7433 claim therefore failed, and the statute offered no avenue for appealing IRS collection actions for someone standing in the beneficiary’s shoes.

Unauthorized Disclosure Claims Under § 7431

Section 7431 provides a damages remedy when a government officer or employee knowingly or negligently discloses a taxpayer’s return or return information in violation of Section 6103 of the tax code.

The statute uses the same limiting language: only “such taxpayer”—the taxpayer whose return or return information was disclosed—can bring the civil action.

Courts have applied this restriction consistently to say that the phrase “such taxpayer” limits standing to the taxpayer whose own return information was disclosed. At least one court has even found that a beneficiary of an estate lacked standing to bring a Section 7431 claim because the disclosed information belonged to the estate, not the beneficiary personally.

The court in the present case reached the same conclusion. The decedent and the estate were the relevant taxpayers. A beneficiary, however directly affected by the fallout from a disclosure, is not “such taxpayer” within the meaning of Section 7431.

The Role the Personal Representative

Here is the question that the Hafner opinion leaves hanging: if the estate had legitimate claims—for refunds, for wrongful levy, for unauthorized collection—who was supposed to bring them?

The answer is probably the personal representative of the estate. Apparently under Washington law, as under the law of most states like Texas, a personal representative owes a fiduciary duty to the estate and its beneficiaries to marshal estate assets. Marshaling means identifying, protecting, and recovering property that belongs to the estate. A wrongful levy claim, a tax overpayment refund, and damages for unauthorized IRS collection are all potential estate assets. The personal representative is the party with both the authority and the legal obligation to pursue them.

But if you read the facts above closely, you’ll recall that the taxpayer here was the estate’s “former administrator”—meaning he had once served as personal representative but apparently was no longer acting in that capacity when the lawsuit was filed. He brought the action explicitly as the estate’s beneficiary, not as its administrator. That capacity distinction turned out to be fatal. It is not clear from the opinion why this was the case.

But had the taxpayer filed as the estate’s personal repre…

But had the taxpayer filed as the estate’s personal representative—or had a currently serving representative filed on the estate’s behalf—the standing analysis under each statute would have looked very different. The estate was the taxpayer, the estate had the interest in the property at the time of the levy, and the estate was the entity from whom the IRS collected. A personal representative suing on behalf of the estate would have been asserting exactly those rights. The court might still have had other issues to address, such as sovereign immunity, but it would not have been able to dismiss on standing alone.

While not addressed in the case

While not addressed in the case, the consequence of filing in the wrong capacity can be a huge problem. The litigation does not necessarily toll the statute of limitations. Since the government acts slowly in litigation cases, and even in audits and collections, those who file in the wrong name may only discover it after the statute for filing the suit again has passed. This can mean that the estate loses its ability to pursue the claims.

The Takeaway

This case is a reminder that the administration of an estate does not end just because the probate court closes its file. When unpaid tax debts or disputed IRS tax collections remain open at the time of death—or when the IRS has taken collection action that may have been improper—those issues carry forward into the estate administration.

They do not simply transfer to the beneficiaries. They stay with the estate, and the estate must address them through its legal representative. Those serving as the personal representative have to act and be actively engaged in identifying and protecting claims like this. When the IRS has taken improper collection action against a decedent or an estate, the estate’s personal representative needs to identify those claims early, assess the limitations periods, and take action before the window closes.

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The Classification algorithm is a supervised learning method that trains data to determine the category of future observations. This is why firstly, let us understand what is supervised learning.

What is Supervised Learning

Understanding Supervised Learning

Supervised learning develops a function to predict a defined label based on the input data.

The model in Supervised Learning learns by action. During training, the model examines which label is related to the given data and, as a result, can identify patterns between the data and particular labels.

Let us understand supervised learning with an example of Speech Recognition. It is an application where you train an algorithm with your voice. Virtual assistants such as Google Assistant and Siri, which recognize and respond to your voice, are the most well-known real-world supervised learning applications.

Supervised Learning might sort data into categories (a classification challenge) or predict a result (regression algorithms). This article will specifically address everything we need to know about classification in Machine Learning.

What is Classification in Machine Learning?

The process of recognizing, interpreting, and classifying objects or thoughts into various groups is known as classification. Machine learning models use a variety of algorithms to classify future datasets into appropriate and relevant categories with the help of already-categorized training datasets.

Classification in Machine Learning

In other words, classification is a type of “pattern recognition.” In this case, classification algorithms applied to training data detect the same pattern (same number sequences, words, etc.) in consecutive data sets.

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Types of Classification Models

There are four primary classification tasks you could come across:

  • Binary Classification
  • Multi-Class Classification
  • Multi-Label Classification
  • Imbalanced Classification

Binary Classification

The term “binary classification” refers to tasks that can provide one of two class labels as an output. In general, one is regarded as the normal state, while the other is abnormal. The following examples can assist you in better comprehending them.

For example, for email spam detection, the normal condition is “not spam,” whereas the abnormal state is “spam.” “Likewise, Cancer not found” is the normal condition of an activity involving a medical test, whereas “cancer identified” is the abnormal state.

The normal state class is usually allocated the class label 0, whereas the abnormal state class is assigned the class label 1.

Some of the popular algorithms used for binary classification are:

  • Decision Trees
  • Logistic Regression
  • Support Vector Machine
  • k-Nearest Neighbors
  • Naive Bayes

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Multi-Label Classification

We refer to multi-label classification tasks as those in which we need to assign two or more distinct class labels that can be predicted for each case. A simple example is photo classification, in which a single shot may contain many items, such as a puppy or an apple, and so on.  In this type of classification, you can predict many labels rather than just one.

The most common algorithms are:

  • Multi-label Random Forests
  • Multi-label Decision trees
  • Multi-label Gradient Boosting

Multi-Class Classification

Tasks that have two or more class labels are called multi-class classification.

The multi-class classification does not differentiate between normal and abnormal results. 

In some situations, the number of class labels might be rather big. For instance, a model may predict that a photograph belongs to one of thousands or tens of thousands of faces in a facial recognition system. Examples are classified into one of several known classes.

Some of the popular algorithms used for multi-class classification are:

  • Naive Bayes
  • k-Nearest Neighbors
  • Random Forest
  • Gradient Boosting
  • Decision Trees

Imbalanced Classification

Imbalanced Classification refers to tasks in which the number of items in each class is distributed unequally. In general, unbalanced classification problems are binary classification tasks in which most of the training dataset belongs to the normal class and just a small percentage to the abnormal class.

Learners in Classification Problem

There are two types of learners in a classification problem, namely:

  • Eager Learners
  • Lazy Learners

Eager Learners

Eager learning occurs when a machine learning algorithm constructs a model shortly after obtaining training data. It’s named eager because the first thing it does when it obtains the data set is, it creates the model. The training data is then forgotten. When new input data arrives, the model is used to evaluate it. The vast majority of machine learning algorithms are eager to learn.

Lazy Learners

Lazy learning, on the other hand, occurs when a machine learning algorithm does not develop a model immediately after receiving training data but instead waits until it is given input data to analyze. It’s named lazy because it waits until it’s absolutely essential to construct a model if it builds any at all. It only saves training data when it receives it. When the input data arrives, it uses the previously stored data to evaluate the output. Instead of learning a discriminative function from the training data, the lazy learning algorithm “memorizes” the training dataset. The eager learning algorithm, on the other hand, learns its model weights (parameters) during training.

Types of Machine learning Classification Algorithms

Classification algorithms use input training data in machine learning to predict the likelihood or probability that the following data will fall into one specified category. One of the most popular classifications used to sort emails into “spam” and “non-spam” categories, as employed by today’s leading email service providers.

They are two types of classification models, namely:

  • Linear Models
  • Non-linear Models

1. Linear Models

Support Vector Machine

Support Vector Machine

The support vector machine (SVM) is a frequently used machine learning technique for classification and regression problems. It is, however, mostly employed to tackle categorization difficulties. SVM’s main goal is to determine the best decision boundaries in an N-dimensional space that can classify data points, and the optimal decision boundary is known as the Hyperplane. The extreme vector is chosen by SVM to locate the hyperplane, and these vectors are referred to as support vectors.

Logistic Regression
Logistic Regression

In logistic regression, the sigmoid function returns the probability of a label. It is used widely when the classification problem is binary, for example, true or false, win or lose, positive or negative.

Logistic regression is used to determine the right fit between a dependent variable and a set of independent variables. Because it quantifies the factors that lead to categorization, it beats alternative binary classification algorithms like KNN.

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2. Non-Linear Models

Decision Tree

Decision Tree

The classification model is developed using the decision tree algorithm as a tree structure. The data is then divided down into smaller structures and connected to an incremental decision tree to complete the process. The final output looks like a tree, complete with nodes and leaves. Using the training data, the rules are learned one by one, one by one. Every time a rule is learned, the tuples that cover the rules are removed. The technique is repeated on the training set until the termination point is reached.

The tree is built using a recursive top-down divide and conquer method. A leaf symbolizes a classification or decision, and a decision node will contain two or more branches. The root node of a decision tree is the highest node that corresponds to the best predictor, and the best thing about a decision tree is that it can handle both category and numerical data.

Kernel SVM

A kernel in SVM is a function that assists in problem resolution. They provide you shortcuts so you don’t have to complete hard calculations. Kernel is remarkable since it allows us to go to higher dimensions and do smooth calculations. It is possible to work with an infinite number of dimensions with kernels.

K-Nearest Neighbor

The K-Nearest Neighbor technique divides data into groups based on the distance between data points and is used for classification and prediction. The K-Nearest Neighbor algorithm implies that data points near together must be similar. Hence, the data point to be classed will be grouped with the closest cluster.

Naive Bayes

The classification algorithm Naive Bayes is based on the assumption that predictors in a dataset are independent. This implies that the features are independent of one another. For example, when given a banana, the classifier will notice that the fruit is yellow in color, rectangular in shape, and long and tapered. These characteristics will add to the likelihood of it becoming a banana in its own right and are not reliant on one another. Naive Bayes is based on the Bayes theorem, which is represented as:

P(A|B) = (P(A) P(B|A)) / P(B)

 Here:
         P(A | B) = how likely B happens
         P(A) = how likely A happens
         P(B) = how likely B happens
         P(B | A) = how likely B happens given that A happen

Stochastic Gradient Descent

It is an extremely effective and simple method for fitting linear models. If the sample data is vast, Stochastic Gradient Descent is beneficial. For classification, it provides a variety of loss functions and penalties.

The only benefit is the ease of implementation and efficiency. Still, stochastic gradient descent has several drawbacks, including the need for many hyper-parameters and sensitivity to feature scaling.

Random Forest

Random Forest

Random decision trees, also known as random forest, may be used for classification, regression, and other tasks. It works by building many decision trees during training and then outputs the class that is the individual trees’ mode, mean, or classification prediction.

A random forest (meta-estimator) fits several trees to different subsamples of data sets and averages the results to increase the model’s predicted accuracy. The sub-sample size is similar to the original input size; however, replacements are frequently used in the samples.

Artificial Neural Networks

Artificial Neural Networks

A neural network uses a model inspired by neurons and their connections in the brain to convert an input vector to an output vector. The model comprises layers of neurons coupled by weights that change the relative relevance of different inputs. Each neuron has an activation function that controls the cell’s output (as a function of its input vector multiplied by its weight vector). The output is calculated by applying the input vector to the network’s input layer, then computing each neuron’s outputs via the network (in a feed-forward fashion).

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Conclusion

In this blog, we looked at what Supervised Learning is and its sub-branch Classification, some of the most widely used classification models, and how to predict their accuracy and see whether they are trained correctly. 

Related Articles:

Feature Selection Techniques In Machine Learning



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